Systems and methods for generating, visualizing and classifying molecular functional profiles

ABSTRACT

Various methods, systems, computer readable media, and graphical user interfaces (GUIs) are presented and described that enable a subject, doctor, or user to characterize or classify various types of cancer precisely. Additionally, described herein are methods, systems, computer readable media, and GUIs that enable more effective specification of treatment and improved outcomes for patients with identified types of cancer. Some embodiments of the methods, systems, computer readable media, and GUIs described herein comprise obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject; identifying an MF profile cluster with which to associate the MF profile for the subject; and clustering the plurality of MF profiles to obtain the MF profile clusters.

RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 120 and is acontinuation of U.S. patent application Ser. No. 16/006,462, filed Jun.12, 2018, entitled “SYSTEMS AND METHODS FOR GENERATING, VISUALIZING ANDCLASSIFYING MOLECULAR FUNCTIONAL PROFILES”, which claims the benefitunder 35 U.S.C. § 119(e) of the filing date of U.S. provisional patentapplication Ser. No. 62/518,787, entitled “SYSTEMS AND METHODS FORIDENTIFYING CANCER TREATMENTS FROM SEQUENCE DATA”, filed Jun. 13, 2017and U.S. provisional patent application Ser. No. 62/598,440, entitled“SYSTEMS AND METHODS IDENTIFYING CANCER TREATMENTS FROM SEQUENCE DATA,”filed Dec. 13, 2017, the entire contents of which are incorporatedherein by reference.

Application Ser. No. 16/006,462 was filed on the same day asInternational Application No.: PCT/US18/37017, entitled “SYSTEMS ANDMETHODS FOR GENERATING, VISUALIZING AND CLASSIFYING MOLECULAR FUNCTIONALPROFILES”, International Application No.: PCT/US18/37018, entitled“SYSTEMS AND METHODS FOR IDENTIFYING RESPONDERS AND NON-RESPONDERS TOIMMUNE CHECKPOINT BLOCKADE THERAPY”, and International Application No.:PCT/US18/37008, entitled “SYSTEMS AND METHODS FOR IDENTIFYING CANCERTREATMENTS FROM NORMALIZED BIOMARKER SCORES”, the entire contents ofeach of which are incorporated herein by reference.

FIELD

Aspects of the technology described herein relate to generating,visualizing and classifying molecular-functional (MF) profiles of cancerpatients.

Some aspects of the technology described herein relate to generating agraphical user interface (GUI) for visualizing a molecular-functionalprofile of a cancer patient.

Some aspects of the technology described herein relate to identifyingthe type of MF profile of a patient, and predicting prognoses,identifying therapies, and/or otherwise aiding in the personalized careof the patient using the identified type.

BACKGROUND

Correctly characterizing the type or types of cancer a patient orsubject has and, potentially, selecting one or more effective therapiesfor the patient can be crucial for the survival and overall wellbeing ofthat patient. Advances in characterizing cancers, predicting prognoses,identifying effective therapies, and otherwise aiding in personalizedcare of patients with cancer are needed.

SUMMARY

Provided herein, inter alia, are systems and methods for generating amolecular-functional (MF) profile for a subject and identifying anexisting MF profile cluster that is associated with the generated MFprofile. Such information, in some embodiments, is output to a user in agraphical user interface (GUI).

Systems and methods for identifying a molecular-functional (MF) profilecluster with which to associate a MF profile for a subject comprises, insome embodiments, obtaining RNA expression data and/or whole exomesequencing (WES) data for the subject; determining a MF profile for thesubject, in part, by determining a gene group expression level for eachgene group in a set of gene groups using the RNA expression data and/orWES data, the set of gene groups comprising gene groups associated withcancer malignancy and different gene groups associated with cancermicroenvironment; and identifying a MF profile cluster with which toassociate the MF profile for the subject from among multiple MF profileclusters that were generated by determining a plurality of MF profilesfor a respective plurality of subjects using RNA expression dataobtained from biological samples for the plurality of subjects, each ofthe plurality of MF profiles containing a gene group expression levelfor each gene group in the set of gene groups, and clustering theplurality of MF profiles to obtain the MF profile clusters. Providedherein, inter alia, are systems and methods for generating MF profileclusters. Such information, in some embodiments, is stored in one ormore databases.

Systems and methods for generating MF profile clusters comprises, insome embodiments, obtaining RNA expression data and/or whole exomesequencing (WES) data for a plurality of subjects having a cancer of aparticular type; determining a respective plurality of MF profiles forthe plurality of subjects, in part, by determining, for each subject, arespective gene group expression level for each gene group in a set ofgene groups using the RNA expression data and/or WES data, the set ofgene groups comprising gene groups associated with cancer malignancy anddifferent gene groups associated with cancer microenvironment;clustering the plurality of MF profiles to obtain MF profile clusterscomprising a first MF profile cluster, a second MF profile cluster, athird MF profile cluster, and a fourth MF profile cluster; and storingthe plurality of MF profiles in association with information identifyingthe particular cancer type.

Provided herein, inter alia, are systems and methods for generating amolecular-functional (MF) profile for a subject using at least four(e.g., at least five) gene group expression levels and identifying anexisting MF profile cluster that is associated with the generated MFprofile. Such information, in some embodiments, is output to a user in agraphical user interface (GUI).

Systems and methods for identifying a molecular-functional (MF) profilecluster with which to associate a MF profile for a subject comprises, insome embodiments, obtaining RNA expression data and/or whole exomesequencing (WES) data for the subject; determining a MF profile for thesubject, in part, by determining a gene group expression level for eachgene group in a set of gene groups using the RNA expression data and/orWES data, the set of gene groups comprising gene groups associated withcancer malignancy that consists of a tumor properties group and genegroups associated with cancer microenvironment that consists of atumor-promoting immune microenvironment group, a an anti-tumor immunemicroenvironment group, an angiogenesis group, and a fibroblasts group;and identifying a MF profile cluster with which to associate the MFprofile for the subject from among multiple MF profile clusters thatwere generated by determining a plurality of MF profiles for arespective plurality of subjects using RNA expression data obtained frombiological samples for the plurality of subjects, each of the pluralityof MF profiles containing a gene group expression level for each genegroup in the set of gene groups, and clustering the plurality of MFprofiles to obtain the MF profile clusters.

Provided herein, inter alia, are systems and methods for generatingmolecular-functional (MF) profile clusters, generating MF profiles for asubject, and associating the patient's MF profile with the MF profilecluster. Such information, in some embodiments, is output to a user in agraphical user interface (GUI).

Systems and methods for generating molecular-functional (MF) profileclusters, generating MF profiles for a subject, and associating thepatient's MF profile with the MF profile cluster comprises, in someembodiments, obtaining RNA expression data and/or whole exome sequencing(WES) data for a plurality of subjects; determining a respectiveplurality of MF profiles for the plurality of subjects, in part, bydetermining, for each subject, a respective gene group expression levelfor each gene group in a set of gene groups using the RNA expressiondata and/or WES data, the set of gene groups comprising gene groupsassociated with cancer malignancy and different gene groups associatedwith cancer microenvironment; clustering the plurality of MF profiles toobtain MF profile clusters comprising a first MF profile cluster, asecond MF profile cluster, a third MF profile cluster, and a fourth MFprofile cluster; obtaining second RNA expression data from a subject,determining a MF profile for the subject, in part, by determining a genegroup expression level for each gene group in the set of gene groupsusing the second RNA expression data; and identifying a MF profilecluster with which to associate the MF profile for the subject fromamong multiple MF profile clusters.

Provided herein, inter alia, are systems and methods for generating a MFprofile and generating a MF portrait for visualizing the MF profile in agraphical user interface (GUI).

Systems and methods for generating a MF profile and generating a MFportrait for visualizing the MF profile in a graphical user interface(GUI) comprises, in some embodiments, obtaining RNA expression dataand/or whole exome sequencing (WES) data for a subject; determining a MFprofile for the subject, in part, by determining a gene group expressionlevel for each gene group in a set of gene groups using the RNAexpression data and/or WES data, the set of gene groups comprising genegroups associated with cancer malignancy and different gene groupsassociated with cancer microenvironment; determining a first visualcharacteristic for a first GUI element using the first gene groupexpression level; determining a second visual characteristic for asecond GUI element using the second gene group expression level;generating a personalized GUI personalized to the subject; andpresenting the generated personalized GUI to a user.

Provided herein, inter alia, are systems and methods for generating a MFprofile by determining expression levels for e.g., four or five genegroups and generating a MF portrait for visualizing the MF profile in agraphical user interface (GUI).

Systems and methods for generating a MF profile by determiningexpression levels for e.g., four or five gene groups and generating a MFportrait for visualizing the MF profile in a graphical user interface(GUI) comprises, in some embodiments, obtaining RNA expression dataand/or whole exome sequencing (WES) data for a subject; determining a MFprofile for the subject, in part, by determining a gene group expressionlevel for each gene group in a set of gene groups using the RNAexpression data and/or WES data, the set of gene groups comprising genegroups associated with cancer malignancy that consists of X and genegroups associated with cancer microenvironment that consist of atumor-promoting immune microenvironment group, an anti-tumor immunemicroenvironment group, an angiogenesis group, and a fibroblasts group;determining a first visual characteristic for a first GUI element usingthe first gene group expression level; determining a second visualcharacteristic for a second GUI element using the second gene groupexpression level; generating a personalized GUI personalized to thesubject; and presenting the generated personalized GUI to a user.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups, the set of genegroups comprising gene groups associated with cancer malignancy anddifferent gene groups associated with cancer microenvironment; andidentifying, from among multiple MF profile clusters, an MF profilecluster with which to associate the MF profile for the subject, the MFprofile clusters comprising: a first MF profile cluster associated withinflamed and vascularized biological samples and/or inflamed andfibroblast-enriched biological samples, a second MF profile clusterassociated with inflamed and non-vascularized biological samples and/orinflamed and non-fibroblast-enriched biological samples, a third MFprofile cluster associated with non-inflamed and vascularized biologicalsamples and/or non-inflamed and fibroblast-enriched biological samples,and a fourth MF profile cluster associated with non-inflamed andnon-vascularized biological samples and/or non-inflamed andnon-fibroblast-enriched biological samples, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using RNA expression data obtained frombiological samples from the plurality of subjects, each of the pluralityof MF profiles containing a gene group expression level for each genegroup in the set of gene groups; and clustering the plurality of MFprofiles to obtain the MF profile clusters.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining RNA expressiondata and/or whole exome sequencing (WES) data for a biological samplefrom a subject; determining a molecular-functional (MF) profile for thesubject at least in part by determining, using the RNA expression data,a gene group expression level for each gene group in a set of genegroups, the set of gene groups comprising gene groups associated withcancer malignancy and different gene groups associated with cancermicroenvironment; and identifying, from among multiple MF profileclusters, an MF profile cluster with which to associate the MF profilefor the subject, the MF profile clusters comprising: a first MF profilecluster associated with inflamed and vascularized biological samplesand/or inflamed and fibroblast-enriched biological samples, a second MFprofile cluster associated with inflamed and non-vascularized biologicalsamples and/or inflamed and non-fibroblast-enriched biological samples,a third MF profile cluster associated with non-inflamed and vascularizedbiological samples and/or non-inflamed and fibroblast-enrichedbiological samples, and a fourth MF profile cluster associated withnon-inflamed and non-vascularized biological samples and/or non-inflamedand non-fibroblast-enriched biological samples, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using RNA expression data obtained frombiological samples from the plurality of subjects, each of the pluralityof MF profiles containing a gene group expression level for each genegroup in the set of gene groups; and clustering the plurality of MFprofiles to obtain the MF profile clusters.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause at least one computer hardware processor to perform:obtaining RNA expression data and/or whole exome sequencing (WES) datafor a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups, the set of genegroups comprising gene groups associated with cancer malignancy anddifferent gene groups associated with cancer microenvironment; andidentifying, from among multiple MF profile clusters, an MF profilecluster with which to associate the MF profile for the subject, the MFprofile clusters comprising: a first MF profile cluster associated withinflamed and vascularized biological samples and/or inflamed andfibroblast-enriched biological samples, a second MF profile clusterassociated with inflamed and non-vascularized biological samples and/orinflamed and non-fibroblast-enriched biological samples, a third MFprofile cluster associated with non-inflamed and vascularized biologicalsamples and/or non-inflamed and fibroblast-enriched biological samples,and a fourth MF profile cluster associated with non-inflamed andnon-vascularized biological samples and/or non-inflamed andnon-fibroblast-enriched biological samples, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using RNA expression data obtained frombiological samples from the plurality of subjects, each of the pluralityof MF profiles containing a gene group expression level for each genegroup in the set of gene groups; and clustering the plurality of MFprofiles to obtain the MF profile clusters.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data from biological samples from a plurality of subjects, atleast some of the subjects having a cancer of a particular type;determining a respective plurality of molecular-functional (MF) profilesfor the plurality of subjects at least in part by, for each of theplurality of subjects, determining, using the RNA expression data, arespective gene group expression level for each group in a set of genegroups, the set of gene groups comprising gene groups associated withcancer malignancy and different gene groups associated with cancermicroenvironment; clustering the plurality of MF profiles to obtain MFprofile clusters comprising: a first MF profile cluster associated withinflamed and vascularized biological samples and/or inflamed andfibroblast-enriched biological samples, a second MF profile clusterassociated with inflamed and non-vascularized biological samples and/orinflamed and non-fibroblast-enriched biological samples, a third MFprofile cluster associated with non-inflamed and vascularized biologicalsamples and/or non-inflamed and fibroblast-enriched biological samples,and a fourth MF profile cluster associated with non-inflamed andnon-vascularized biological samples and/or non-inflamed andnon-fibroblast-enriched biological sample; and storing the plurality ofMF profiles in association with information identifying the particularcancer type.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining RNA expressiondata and/or whole exome sequencing (WES) data from biological samplesfrom a plurality of subjects, at least some of the subjects having acancer of a particular type; determining a respective plurality ofmolecular-functional (MF) profiles for the plurality of subjects atleast in part by, for each of the plurality of subjects, determining,using the RNA expression data, a respective gene group expression levelfor each group in a set of gene groups, the set of gene groupscomprising gene groups associated with cancer malignancy and differentgene groups associated with cancer microenvironment; clustering theplurality of MF profiles to obtain MF profile clusters comprising: afirst MF profile cluster associated with inflamed and vascularizedbiological samples and/or inflamed and fibroblast-enriched biologicalsamples, a second MF profile cluster associated with inflamed andnon-vascularized biological samples and/or inflamed andnon-fibroblast-enriched biological samples, a third MF profile clusterassociated with non-inflamed and vascularized biological samples and/ornon-inflamed and fibroblast-enriched biological samples, and a fourth MFprofile cluster associated with non-inflamed and non-vascularizedbiological samples and/or non-inflamed and non-fibroblast-enrichedbiological sample; and storing the plurality of MF profiles inassociation with information identifying the particular cancer type.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause at least one computer hardware processor to perform:obtaining RNA expression data and/or whole exome sequencing (WES) datafrom biological samples from a plurality of subjects, at least some ofthe subjects having a cancer of a particular type; determining arespective plurality of molecular-functional (MF) profiles for theplurality of subjects at least in part by, for each of the plurality ofsubjects, determining, using the RNA expression data, a respective genegroup expression level for each group in a set of gene groups, the setof gene groups comprising gene groups associated with cancer malignancyand different gene groups associated with cancer microenvironment;clustering the plurality of MF profiles to obtain MF profile clusterscomprising: a first MF profile cluster associated with inflamed andvascularized biological samples and/or inflamed and fibroblast-enrichedbiological samples, a second MF profile cluster associated with inflamedand non-vascularized biological samples and/or inflamed andnon-fibroblast-enriched biological samples, a third MF profile clusterassociated with non-inflamed and vascularized biological samples and/ornon-inflamed and fibroblast-enriched biological samples, and a fourth MFprofile cluster associated with non-inflamed and non-vascularizedbiological samples and/or non-inflamed and non-fibroblast-enrichedbiological sample; and storing the plurality of MF profiles inassociation with information identifying the particular cancer type.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups, the set of genegroups comprising a first gene group associated with cancer malignancyand a second gene group associated with cancer microenvironment, whereinthe first and second gene groups are different, the determiningcomprising: determining a first gene group expression level for thefirst gene group, and determining a second gene group expression levelfor the second gene group; determining a first visual characteristic fora first graphical user interface (GUI) element using the first genegroup expression level; determining a second visual characteristic for asecond GUI element using the second gene group expression level;generating a personalized GUI personalized to the subject, the GUIcomprising: a first GUI portion associated with cancer malignancy andcontaining the first GUI element having the first visual characteristic,and a second GUI portion associated with cancer microenvironment andcontaining the second GUI element having the second visualcharacteristic; and presenting the generated personalized GUI to a user.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining RNA expressiondata and/or whole exome sequencing (WES) data for a biological samplefrom a subject; determining a molecular-functional (MF) profile for thesubject at least in part by determining, using the RNA expression data,a gene group expression level for each gene group in a set of genegroups, the set of gene groups comprising a first gene group associatedwith cancer malignancy and a second gene group associated with cancermicroenvironment, wherein the first and second gene groups aredifferent, the determining comprising: determining a first gene groupexpression level for the first gene group, and determining a second genegroup expression level for the second gene group; determining a firstvisual characteristic for a first graphical user interface (GUI) elementusing the first gene group expression level; determining a second visualcharacteristic for a second GUI element using the second gene groupexpression level; generating a personalized GUI personalized to thesubject, the GUI comprising: a first GUI portion associated with cancermalignancy and containing the first GUI element having the first visualcharacteristic, and a second GUI portion associated with cancermicroenvironment and containing the second GUI element having the secondvisual characteristic; and presenting the generated personalized GUI toa user.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause at least one computer hardware processor to perform:obtaining RNA expression data and/or whole exome sequencing (WES) datafor a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups, the set of genegroups comprising a first gene group associated with cancer malignancyand a second gene group associated with cancer microenvironment, whereinthe first and second gene groups are different, the determiningcomprising: determining a first gene group expression level for thefirst gene group, and determining a second gene group expression levelfor the second gene group; determining a first visual characteristic fora first graphical user interface (GUI) element using the first genegroup expression level; determining a second visual characteristic for asecond GUI element using the second gene group expression level;generating a personalized GUI personalized to the subject, the GUIcomprising: a first GUI portion associated with cancer malignancy andcontaining the first GUI element having the first visual characteristic,and a second GUI portion associated with cancer microenvironment andcontaining the second GUI element having the second visualcharacteristic; and presenting the generated personalized GUI to a user.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject having a particulartype of cancer; determining a molecular-functional (MF) profile for thesubject at least in part by: determining, using the RNA expression dataand reference RNA expression data, a gene group expression level foreach gene group in a first set of gene groups associated with cancermalignancy and consisting of the tumor properties group; anddetermining, using the RNA expression data and the reference RNAexpression data, a gene group expression level for each gene group in asecond set of gene groups associated with cancer microenvironment andconsisting of the tumor-promoting immune microenvironment group, theanti-tumor immune microenvironment group, the angiogenesis group, andthe fibroblasts group; and accessing information specifying multiple MFprofile clusters for the particular cancer type; identifying, from amongthe multiple MF profile clusters, an MF profile cluster with which toassociate the MF profile for the subject, the MF profile clusterscomprising: a first MF profile cluster associated with inflamed andvascularized biological samples and/or inflamed and fibroblast-enrichedbiological samples, a second MF profile cluster associated with inflamedand non-vascularized biological samples and/or inflamed andnon-fibroblast-enriched biological samples, a third MF profile clusterassociated with non-inflamed and vascularized biological samples and/ornon-inflamed and fibroblast-enriched biological samples, and a fourth MFprofile cluster associated with non-inflamed and non-vascularizedbiological samples and/or non-inflamed and non-fibroblast-enrichedbiological sample, wherein the MF profile clusters were generated by:determining a plurality of MF profiles for a respective plurality ofsubjects using the reference RNA expression data and RNA expression datafrom biological samples obtained from the plurality of subjects, each ofthe plurality of MF profiles containing a gene group expression levelfor each gene group in the set of gene groups; and clustering theplurality of MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining RNA expressiondata and/or whole exome sequencing (WES) data for a biological samplefrom a subject having a particular type of cancer; determining amolecular-functional (MF) profile for the subject at least in part by:determining, using the RNA expression data and reference RNA expressiondata, a gene group expression level for each gene group in a first setof gene groups associated with cancer malignancy and consisting of thetumor properties group; and determining, using the RNA expression dataand the reference RNA expression data, a gene group expression level foreach gene group in a second set of gene groups associated with cancermicroenvironment and consisting of the tumor-promoting immunemicroenvironment group, the anti-tumor immune microenvironment group,the angiogenesis group, and the fibroblasts group; and accessinginformation specifying multiple MF profile clusters for the particularcancer type; identifying, from among the multiple MF profile clusters,an MF profile cluster with which to associate the MF profile for thesubject, the MF profile clusters comprising: a first MF profile clusterassociated with inflamed and vascularized biological samples and/orinflamed and fibroblast-enriched biological samples, a second MF profilecluster associated with inflamed and non-vascularized biological samplesand/or inflamed and non-fibroblast-enriched biological samples, a thirdMF profile cluster associated with non-inflamed and vascularizedbiological samples and/or non-inflamed and fibroblast-enrichedbiological samples, and a fourth MF profile cluster associated withnon-inflamed and non-vascularized biological samples and/or non-inflamedand non-fibroblast-enriched biological sample, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using the reference RNA expression dataand RNA expression data from biological samples obtained from theplurality of subjects, each of the plurality of MF profiles containing agene group expression level for each gene group in the set of genegroups; and clustering the plurality of MF profiles to obtain the MFprofile clusters.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject having a particulartype of cancer; determining a molecular-functional (MF) profile for thesubject at least in part by: determining, using the RNA expression dataand reference RNA expression data, a gene group expression level foreach gene group in a first set of gene groups associated with cancermalignancy and consisting of the tumor properties group; anddetermining, using the RNA expression data and the reference RNAexpression data, a gene group expression level for each gene group in asecond set of gene groups associated with cancer microenvironment andconsisting of the tumor-promoting immune microenvironment group, theanti-tumor immune microenvironment group, the angiogenesis group, andthe fibroblasts group; and accessing information specifying multiple MFprofile clusters for the particular cancer type; identifying, from amongthe multiple MF profile clusters, an MF profile cluster with which toassociate the MF profile for the subject, the MF profile clusterscomprising: a first MF profile cluster associated with inflamed andvascularized biological samples and/or inflamed and fibroblast-enrichedbiological samples, a second MF profile cluster associated with inflamedand non-vascularized biological samples and/or inflamed andnon-fibroblast-enriched biological samples, a third MF profile clusterassociated with non-inflamed and vascularized biological samples and/ornon-inflamed and fibroblast-enriched biological samples, and a fourth MFprofile cluster associated with non-inflamed and non-vascularizedbiological samples and/or non-inflamed and non-fibroblast-enrichedbiological sample, wherein the MF profile clusters were generated by:determining a plurality of MF profiles for a respective plurality ofsubjects using the reference RNA expression data and RNA expression datafrom biological samples obtained from the plurality of subjects, each ofthe plurality of MF profiles containing a gene group expression levelfor each gene group in the set of gene groups; and clustering theplurality of MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject having a particulartype of cancer; determining a molecular-functional (MF) profile for thesubject at least in part by: determining, using the RNA expression dataand reference RNA expression data, a gene group expression level foreach gene group in a first set of gene groups associated with cancermalignancy and consisting of the proliferation rate group, thePI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, thereceptor tyrosine kinases expression group, the tumor suppressors group,the metastasis signature group, the anti-metastatic factors group, andthe mutation status group; and determining, using the RNA expressiondata and the reference RNA expression data, a gene group expressionlevel for each gene group in a second set of gene groups associated withcancer microenvironment and consisting of the antigen presentationgroup, the cytotoxic T and NK cells group, the B cells group, theanti-tumor microenvironment group, the checkpoint inhibition group, theTreg group, the MDSC group, the granulocytes group, the cancerassociated fibroblasts group, the angiogenesis group, and thetumor-promotive immune group; and accessing information specifyingmultiple MF profile clusters for the particular cancer type;identifying, from among the multiple MF profile clusters, an MF profilecluster with which to associate the MF profile for the subject, the MFprofile clusters comprising: a first MF profile cluster associated withinflamed and vascularized biological samples and/or inflamed andfibroblast-enriched biological samples, a second MF profile clusterassociated with inflamed and non-vascularized biological samples and/orinflamed and non-fibroblast-enriched biological samples, a third MFprofile cluster associated with non-inflamed and vascularized biologicalsamples and/or non-inflamed and fibroblast-enriched biological samples,and a fourth MF profile cluster associated with non-inflamed andnon-vascularized biological samples and/or non-inflamed andnon-fibroblast-enriched biological samples, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using the reference RNA expression dataand RNA expression data from biological samples obtained from theplurality of subjects, each of the plurality of MF profiles containing agene group expression level for each gene group in the set of genegroups; and clustering the plurality of MF profiles to obtain the MFprofile clusters.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining RNA expressiondata and/or whole exome sequencing (WES) data for a biological samplefrom a subject having a particular type of cancer; determining amolecular-functional (MF) profile for the subject at least in part by:determining, using the RNA expression data and reference RNA expressiondata, a gene group expression level for each gene group in a first setof gene groups associated with cancer malignancy and consisting of theproliferation rate group, the PI3K/AKT/mTOR signaling group, theRAS/RAF/MEK signaling group, the receptor tyrosine kinases expressiongroup, the tumor suppressors group, the metastasis signature group, theanti-metastatic factors group, and the mutation status group; anddetermining, using the RNA expression data and the reference RNAexpression data, a gene group expression level for each gene group in asecond set of gene groups associated with cancer microenvironment andconsisting of the antigen presentation group, the cytotoxic T and NKcells group, the B cells group, the anti-tumor microenvironment group,the checkpoint inhibition group, the Treg group, the MDSC group, thegranulocytes group, the cancer associated fibroblasts group, theangiogenesis group, and the tumor-promotive immune group; and accessinginformation specifying multiple MF profile clusters for the particularcancer type; identifying, from among the multiple MF profile clusters,an MF profile cluster with which to associate the MF profile for thesubject, the MF profile clusters comprising: a first MF profile clusterassociated with inflamed and vascularized biological samples and/orinflamed and fibroblast-enriched biological samples, a second MF profilecluster associated with inflamed and non-vascularized biological samplesand/or inflamed and non-fibroblast-enriched biological samples, a thirdMF profile cluster associated with non-inflamed and vascularizedbiological samples and/or non-inflamed and fibroblast-enrichedbiological samples, and a fourth MF profile cluster associated withnon-inflamed and non-vascularized biological samples and/or non-inflamedand non-fibroblast-enriched biological samples, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using the reference RNA expression dataand RNA expression data from biological samples obtained from theplurality of subjects, each of the plurality of MF profiles containing agene group expression level for each gene group in the set of genegroups; and clustering the plurality of MF profiles to obtain the MFprofile clusters.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject having a particulartype of cancer; determining a molecular-functional (MF) profile for thesubject at least in part by: determining, using the RNA expression dataand reference RNA expression data, a gene group expression level foreach gene group in a first set of gene groups associated with cancermalignancy and consisting of the proliferation rate group, thePI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, thereceptor tyrosine kinases expression group, the tumor suppressors group,the metastasis signature group, the anti-metastatic factors group, andthe mutation status group; and determining, using the RNA expressiondata and the reference RNA expression data, a gene group expressionlevel for each gene group in a second set of gene groups associated withcancer microenvironment and consisting of the antigen presentationgroup, the cytotoxic T and NK cells group, the B cells group, theanti-tumor microenvironment group, the checkpoint inhibition group, theTreg group, the MDSC group, the granulocytes group, the cancerassociated fibroblasts group, the angiogenesis group, and thetumor-promotive immune group; and accessing information specifyingmultiple MF profile clusters for the particular cancer type;identifying, from among the multiple MF profile clusters, an MF profilecluster with which to associate the MF profile for the subject, the MFprofile clusters comprising: a first MF profile cluster associated withinflamed and vascularized biological samples and/or inflamed andfibroblast-enriched biological samples, a second MF profile clusterassociated with inflamed and non-vascularized biological samples and/orinflamed and non-fibroblast-enriched biological samples, a third MFprofile cluster associated with non-inflamed and vascularized biologicalsamples and/or non-inflamed and fibroblast-enriched biological samples,and a fourth MF profile cluster associated with non-inflamed andnon-vascularized biological samples and/or non-inflamed andnon-fibroblast-enriched biological samples, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using the reference RNA expression dataand RNA expression data from biological samples obtained from theplurality of subjects, each of the plurality of MF profiles containing agene group expression level for each gene group in the set of genegroups; and clustering the plurality of MF profiles to obtain the MFprofile clusters.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject having a particulartype of cancer; determining a molecular-functional (MF) profile for thesubject at least in part by: determining, using the RNA expression dataand reference RNA expression data, a gene group expression level foreach gene group in a first set of gene groups associated with cancermalignancy and consisting of the proliferation rate group, thePI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, thereceptor tyrosine kinases expression group, the growth factors group,the tumor suppressors group, the metastasis signature group, theanti-metastatic factors group, and the mutation status group; anddetermining, using the RNA expression data and the reference RNAexpression data, a gene group expression level for each gene group in asecond set of gene groups associated with cancer microenvironment andconsisting of the MHCI group, the MHCII group, the coactivationmolecules group, the effector cells group, the NK cells group, the Tcell traffic group, the T cells group, the B cells group, the M1signatures group, the Th1 signature group, the antitumor cytokinesgroup, the checkpoint inhibition group, the Treg group, the MDSC group,the granulocytes group, the M2 signature group, the Th2 signature group,the protumor cytokines group, the cancer associated fibroblasts group,the angiogenesis group, and the complement inhibition group; andaccessing information specifying multiple MF profile clusters for theparticular cancer type; identifying, from among the multiple MF profileclusters, an MF profile cluster with which to associate the MF profilefor the subject, the MF profile clusters comprising: a first MF profilecluster associated with inflamed and vascularized biological samplesand/or inflamed and fibroblast-enriched biological samples, a second MFprofile cluster associated with inflamed and non-vascularized biologicalsamples and/or inflamed and non-fibroblast-enriched biological samples,a third MF profile cluster associated with non-inflamed and vascularizedbiological samples and/or non-inflamed and fibroblast-enrichedbiological samples, and a fourth MF profile cluster associated withnon-inflamed and non-vascularized biological samples and/or non-inflamedand non-fibroblast-enriched biological samples, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using the reference RNA expression dataand RNA expression data from biological samples obtained from theplurality of subjects, each of the plurality of MF profiles containing agene group expression level for each gene group in the set of genegroups; and clustering the plurality of MF profiles to obtain the MFprofile clusters.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining RNA expressiondata and/or whole exome sequencing (WES) data for a biological samplefrom a subject having a particular type of cancer; determining amolecular-functional (MF) profile for the subject at least in part by:determining, using the RNA expression data and reference RNA expressiondata, a gene group expression level for each gene group in a first setof gene groups associated with cancer malignancy and consisting of theproliferation rate group, the PI3K/AKT/mTOR signaling group, theRAS/RAF/MEK signaling group, the receptor tyrosine kinases expressiongroup, the growth factors group, the tumor suppressors group, themetastasis signature group, the anti-metastatic factors group, and themutation status group; and determining, using the RNA expression dataand the reference RNA expression data, a gene group expression level foreach gene group in a second set of gene groups associated with cancermicroenvironment and consisting of the MHCI group, the MHCII group, thecoactivation molecules group, the effector cells group, the NK cellsgroup, the T cell traffic group, the T cells group, the B cells group,the M1 signatures group, the Th1 signature group, the antitumorcytokines group, the checkpoint inhibition group, the Treg group, theMDSC group, the granulocytes group, the M2 signature group, the Th2signature group, the protumor cytokines group, the cancer associatedfibroblasts group, the angiogenesis group, and the complement inhibitiongroup; and accessing information specifying multiple MF profile clustersfor the particular cancer type; identifying, from among the multiple MFprofile clusters, an MF profile cluster with which to associate the MFprofile for the subject, the MF profile clusters comprising: a first MFprofile cluster associated with inflamed and vascularized biologicalsamples and/or inflamed and fibroblast-enriched biological samples, asecond MF profile cluster associated with inflamed and non-vascularizedbiological samples and/or inflamed and non-fibroblast-enrichedbiological samples, a third MF profile cluster associated withnon-inflamed and vascularized biological samples and/or non-inflamed andfibroblast-enriched biological samples, and a fourth MF profile clusterassociated with non-inflamed and non-vascularized biological samplesand/or non-inflamed and non-fibroblast-enriched biological samples,wherein the MF profile clusters were generated by: determining aplurality of MF profiles for a respective plurality of subjects usingthe reference RNA expression data and RNA expression data frombiological samples obtained from the plurality of subjects, each of theplurality of MF profiles containing a gene group expression level foreach gene group in the set of gene groups; and clustering the pluralityof MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject having a particulartype of cancer; determining a molecular-functional (MF) profile for thesubject at least in part by: determining, using the RNA expression dataand reference RNA expression data, a gene group expression level foreach gene group in a first set of gene groups associated with cancermalignancy and consisting of the proliferation rate group, thePI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, thereceptor tyrosine kinases expression group, the growth factors group,the tumor suppressors group, the metastasis signature group, theanti-metastatic factors group, and the mutation status group; anddetermining, using the RNA expression data and the reference RNAexpression data, a gene group expression level for each gene group in asecond set of gene groups associated with cancer microenvironment andconsisting of the MHCI group, the MHCII group, the coactivationmolecules group, the effector cells group, the NK cells group, the Tcell traffic group, the T cells group, the B cells group, the M1signatures group, the Th1 signature group, the antitumor cytokinesgroup, the checkpoint inhibition group, the Treg group, the MDSC group,the granulocytes group, the M2 signature group, the Th2 signature group,the protumor cytokines group, the cancer associated fibroblasts group,the angiogenesis group, and the complement inhibition group; andaccessing information specifying multiple MF profile clusters for theparticular cancer type; identifying, from among the multiple MF profileclusters, an MF profile cluster with which to associate the MF profilefor the subject, the MF profile clusters comprising: a first MF profilecluster associated with inflamed and vascularized biological samplesand/or inflamed and fibroblast-enriched biological samples, a second MFprofile cluster associated with inflamed and non-vascularized biologicalsamples and/or inflamed and non-fibroblast-enriched biological samples,a third MF profile cluster associated with non-inflamed and vascularizedbiological samples and/or non-inflamed and fibroblast-enrichedbiological samples, and a fourth MF profile cluster associated withnon-inflamed and non-vascularized biological samples and/or non-inflamedand non-fibroblast-enriched biological samples, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using the reference RNA expression dataand RNA expression data from biological samples obtained from theplurality of subjects, each of the plurality of MF profiles containing agene group expression level for each gene group in the set of genegroups; and clustering the plurality of MF profiles to obtain the MFprofile clusters.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining first RNA expression data and/or first whole exomesequencing (WES) data from biological samples from a plurality ofsubjects; determining a respective plurality of molecular-functional(MF) profiles for the plurality of subjects at least in part by, foreach of the plurality of subjects, determining, using the first RNAexpression data, a respective gene group expression level for each groupin a set of gene groups, the set of gene groups comprising gene groupsassociated with cancer malignancy and different gene groups associatedwith cancer microenvironment; clustering the plurality of MF profiles toobtain MF profile clusters including: a first MF profile clusterassociated with inflamed and vascularized biological samples and/orinflamed and fibroblast-enriched biological samples, a second MF profilecluster associated with inflamed and non-vascularized biological samplesand/or inflamed and non-fibroblast-enriched biological samples, a thirdMF profile cluster associated with non-inflamed and vascularizedbiological samples and/or non-inflamed and fibroblast-enrichedbiological samples, and a fourth MF profile cluster associated withnon-inflamed and non-vascularized biological samples and/or non-inflamedand non-fibroblast-enriched biological samples; obtaining second RNAexpression data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the second RNA expression data, a gene groupexpression level for each group in the set of gene groups; andidentifying, from among the MF profile clusters, a particular MF profilecluster with which to associate the MF profile for the subject.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining first RNAexpression data and/or first whole exome sequencing (WES) data frombiological samples from a plurality of subjects; determining arespective plurality of molecular-functional (MF) profiles for theplurality of subjects at least in part by, for each of the plurality ofsubjects, determining, using the first RNA expression data, a respectivegene group expression level for each group in a set of gene groups, theset of gene groups comprising gene groups associated with cancermalignancy and different gene groups associated with cancermicroenvironment; clustering the plurality of MF profiles to obtain MFprofile clusters including: a first MF profile cluster associated withinflamed and vascularized biological samples and/or inflamed andfibroblast-enriched biological samples, a second MF profile clusterassociated with inflamed and non-vascularized biological samples and/orinflamed and non-fibroblast-enriched biological samples, a third MFprofile cluster associated with non-inflamed and vascularized biologicalsamples and/or non-inflamed and fibroblast-enriched biological samples,and a fourth MF profile cluster associated with non-inflamed andnon-vascularized biological samples and/or non-inflamed andnon-fibroblast-enriched biological samples; obtaining second RNAexpression data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the second RNA expression data, a gene groupexpression level for each group in the set of gene groups; andidentifying, from among the MF profile clusters, a particular MF profilecluster with which to associate the MF profile for the subject.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining first RNA expression data and/or first whole exomesequencing (WES) data from biological samples from a plurality ofsubjects; determining a respective plurality of molecular-functional(MF) profiles for the plurality of subjects at least in part by, foreach of the plurality of subjects, determining, using the first RNAexpression data, a respective gene group expression level for each groupin a set of gene groups, the set of gene groups comprising gene groupsassociated with cancer malignancy and different gene groups associatedwith cancer microenvironment; clustering the plurality of MF profiles toobtain MF profile clusters including: a first MF profile clusterassociated with inflamed and vascularized biological samples and/orinflamed and fibroblast-enriched biological samples, a second MF profilecluster associated with inflamed and non-vascularized biological samplesand/or inflamed and non-fibroblast-enriched biological samples, a thirdMF profile cluster associated with non-inflamed and vascularizedbiological samples and/or non-inflamed and fibroblast-enrichedbiological samples, and a fourth MF profile cluster associated withnon-inflamed and non-vascularized biological samples and/or non-inflamedand non-fibroblast-enriched biological samples; obtaining second RNAexpression data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the second RNA expression data, a gene groupexpression level for each group in the set of gene groups; andidentifying, from among the MF profile clusters, a particular MF profilecluster with which to associate the MF profile for the subject.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups comprising: first genegroups associated with cancer malignancy consisting of the tumorproperties group; and second gene groups associated with cancermicroenvironment consisting of the tumor-promoting immunemicroenvironment group, the anti-tumor immune microenvironment group,the angiogenesis group, and the fibroblasts group, determining a firstset of visual characteristics for a first plurality of graphical userinterface (GUI) elements using the gene group expression levelsdetermined for the first gene groups; determining a second set of visualcharacteristics for a second plurality of GUI elements using the genegroup expression levels determined for the second gene groups;generating a personalized GUI personalized to the subject, thegenerating comprising: generating a first GUI portion associated withcancer malignancy and containing the first plurality of GUI elementshaving the determined first set of visual characteristics; andgenerating a second GUI portion associated with cancer microenvironmentand containing the second plurality of GUI elements having thedetermined second set of visual characteristics; and presenting thegenerated personalized GUI to a user.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining RNA expressiondata and/or whole exome sequencing (WES) data for a biological samplefrom a subject; determining a molecular-functional (MF) profile for thesubject at least in part by determining, using the RNA expression data,a gene group expression level for each gene group in a set of genegroups comprising: first gene groups associated with cancer malignancyconsisting of the tumor properties group; and second gene groupsassociated with cancer microenvironment consisting of thetumor-promoting immune microenvironment group, the anti-tumor immunemicroenvironment group, the angiogenesis group, and the fibroblastsgroup, determining a first set of visual characteristics for a firstplurality of graphical user interface (GUI) elements using the genegroup expression levels determined for the first gene groups;determining a second set of visual characteristics for a secondplurality of GUI elements using the gene group expression levelsdetermined for the second gene groups; generating a personalized GUIpersonalized to the subject, the generating comprising: generating afirst GUI portion associated with cancer malignancy and containing thefirst plurality of GUI elements having the determined first set ofvisual characteristics; and generating a second GUI portion associatedwith cancer microenvironment and containing the second plurality of GUIelements having the determined second set of visual characteristics; andpresenting the generated personalized GUI to a user.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups comprising: first genegroups associated with cancer malignancy consisting of the tumorproperties group; and second gene groups associated with cancermicroenvironment consisting of the tumor-promoting immunemicroenvironment group, the anti-tumor immune microenvironment group,the angiogenesis group, and the fibroblasts group, determining a firstset of visual characteristics for a first plurality of graphical userinterface (GUI) elements using the gene group expression levelsdetermined for the first gene groups; determining a second set of visualcharacteristics for a second plurality of GUI elements using the genegroup expression levels determined for the second gene groups;generating a personalized GUI personalized to the subject, thegenerating comprising: generating a first GUI portion associated withcancer malignancy and containing the first plurality of GUI elementshaving the determined first set of visual characteristics; andgenerating a second GUI portion associated with cancer microenvironmentand containing the second plurality of GUI elements having thedetermined second set of visual characteristics; and presenting thegenerated personalized GUI to a user.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups comprising: first genegroups associated with cancer malignancy consisting of the proliferationrate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signalinggroup, the receptor tyrosine kinases expression group, the tumorsuppressors group, the metastasis signature group, the anti-metastaticfactors group, and the mutation status group; and second gene groupsassociated with cancer microenvironment consisting of the cancerassociated fibroblasts group, the angiogenesis group, the antigenpresentation group, the cytotoxic T and NK cells group, the B cellsgroup, the anti-tumor microenvironment group, the checkpoint inhibitiongroup, the Treg group, the MDSC group, the granulocytes group, and thetumor-promotive immune group; determining a first set of visualcharacteristics for a first plurality of graphical user interface (GUI)elements using the gene group expression levels determined for the firstgene groups; determining a second set of visual characteristics for asecond plurality of GUI elements using the gene group expression levelsdetermined for the second gene groups; generating a personalized GUIpersonalized to the subject, the generating comprising: generating afirst GUI portion associated with cancer malignancy and containing thefirst plurality of GUI elements having the determined first set ofvisual characteristics; and generating a second GUI portion associatedwith cancer microenvironment and containing the second plurality of GUIelements having the determined second set of visual characteristics; andpresenting the generated personalized GUI to a user.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining RNA expressiondata and/or whole exome sequencing (WES) data for a biological samplefrom a subject; determining a molecular-functional (MF) profile for thesubject at least in part by determining, using the RNA expression data,a gene group expression level for each gene group in a set of genegroups comprising: first gene groups associated with cancer malignancyconsisting of the proliferation rate group, the PI3K/AKT/mTOR signalinggroup, the RAS/RAF/MEK signaling group, the receptor tyrosine kinasesexpression group, the tumor suppressors group, the metastasis signaturegroup, the anti-metastatic factors group, and the mutation status group;and second gene groups associated with cancer microenvironmentconsisting of the cancer associated fibroblasts group, the angiogenesisgroup, the antigen presentation group, the cytotoxic T and NK cellsgroup, the B cells group, the anti-tumor microenvironment group, thecheckpoint inhibition group, the Treg group, the MDSC group, thegranulocytes group, and the tumor-promotive immune group; determining afirst set of visual characteristics for a first plurality of graphicaluser interface (GUI) elements using the gene group expression levelsdetermined for the first gene groups; determining a second set of visualcharacteristics for a second plurality of GUI elements using the genegroup expression levels determined for the second gene groups;generating a personalized GUI personalized to the subject, thegenerating comprising: generating a first GUI portion associated withcancer malignancy and containing the first plurality of GUI elementshaving the determined first set of visual characteristics; andgenerating a second GUI portion associated with cancer microenvironmentand containing the second plurality of GUI elements having thedetermined second set of visual characteristics; and presenting thegenerated personalized GUI to a user.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups comprising: first genegroups associated with cancer malignancy consisting of the proliferationrate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signalinggroup, the receptor tyrosine kinases expression group, the tumorsuppressors group, the metastasis signature group, the anti-metastaticfactors group, and the mutation status group; and second gene groupsassociated with cancer microenvironment consisting of the cancerassociated fibroblasts group, the angiogenesis group, the antigenpresentation group, the cytotoxic T and NK cells group, the B cellsgroup, the anti-tumor microenvironment group, the checkpoint inhibitiongroup, the Treg group, the MDSC group, the granulocytes group, and thetumor-promotive immune group; determining a first set of visualcharacteristics for a first plurality of graphical user interface (GUI)elements using the gene group expression levels determined for the firstgene groups; determining a second set of visual characteristics for asecond plurality of GUI elements using the gene group expression levelsdetermined for the second gene groups; generating a personalized GUIpersonalized to the subject, the generating comprising: generating afirst GUI portion associated with cancer malignancy and containing thefirst plurality of GUI elements having the determined first set ofvisual characteristics; and generating a second GUI portion associatedwith cancer microenvironment and containing the second plurality of GUIelements having the determined second set of visual characteristics; andpresenting the generated personalized GUI to a user.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups comprising: first genegroups associated with cancer malignancy consisting of the proliferationrate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signalinggroup, the receptor tyrosine kinases expression group, the growthfactors group, the tumor suppressors group, the metastasis signaturegroup, the anti-metastatic factors group, and the mutation status group;and second gene groups associated with cancer microenvironmentconsisting of the cancer associated fibroblasts group, the angiogenesisgroup, the MHCI group, the MHCII group, the coactivation moleculesgroup, the effector cells group, the NK cells group, the T cell trafficgroup, the T cells group, the B cells group, the M1 signatures group,the Th1 signature group, the antitumor cytokines group, the checkpointinhibition group, the Treg group, the MDSC group, the granulocytesgroup, the M2 signature group, the Th2 signature group, the protumorcytokines group, and the complement inhibition group; determining afirst set of visual characteristics for a first plurality of graphicaluser interface (GUI) elements using the gene group expression levelsdetermined for the first gene groups; determining a second set of visualcharacteristics for a second plurality of GUI elements using the genegroup expression levels determined for the second gene groups;generating a personalized GUI personalized to the subject, thegenerating comprising: generating a first GUI portion associated withcancer malignancy and containing the first plurality of GUI elementshaving the determined first set of visual characteristics; andgenerating a second GUI portion associated with cancer microenvironmentand containing the second plurality of GUI elements having thedetermined second set of visual characteristics; and presenting thegenerated personalized GUI to a user.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining RNA expressiondata and/or whole exome sequencing (WES) data for a biological samplefrom a subject; determining a molecular-functional (MF) profile for thesubject at least in part by determining, using the RNA expression data,a gene group expression level for each gene group in a set of genegroups comprising: first gene groups associated with cancer malignancyconsisting of the proliferation rate group, the PI3K/AKT/mTOR signalinggroup, the RAS/RAF/MEK signaling group, the receptor tyrosine kinasesexpression group, the growth factors group, the tumor suppressors group,the metastasis signature group, the anti-metastatic factors group, andthe mutation status group; and second gene groups associated with cancermicroenvironment consisting of the cancer associated fibroblasts group,the angiogenesis group, the MHCI group, the MHCII group, thecoactivation molecules group, the effector cells group, the NK cellsgroup, the T cell traffic group, the T cells group, the B cells group,the M1 signatures group, the Th1 signature group, the antitumorcytokines group, the checkpoint inhibition group, the Treg group, theMDSC group, the granulocytes group, the M2 signature group, the Th2signature group, the protumor cytokines group, and the complementinhibition group; determining a first set of visual characteristics fora first plurality of graphical user interface (GUI) elements using thegene group expression levels determined for the first gene groups;determining a second set of visual characteristics for a secondplurality of GUI elements using the gene group expression levelsdetermined for the second gene groups; generating a personalized GUIpersonalized to the subject, the generating comprising: generating afirst GUI portion associated with cancer malignancy and containing thefirst plurality of GUI elements having the determined first set ofvisual characteristics; and generating a second GUI portion associatedwith cancer microenvironment and containing the second plurality of GUIelements having the determined second set of visual characteristics; andpresenting the generated personalized GUI to a user.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups comprising: first genegroups associated with cancer malignancy consisting of the proliferationrate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signalinggroup, the receptor tyrosine kinases expression group, the growthfactors group, the tumor suppressors group, the metastasis signaturegroup, the anti-metastatic factors group, and the mutation status group;and second gene groups associated with cancer microenvironmentconsisting of the cancer associated fibroblasts group, the angiogenesisgroup, the MHCI group, the MHCII group, the coactivation moleculesgroup, the effector cells group, the NK cells group, the T cell trafficgroup, the T cells group, the B cells group, the M1 signatures group,the Th1 signature group, the antitumor cytokines group, the checkpointinhibition group, the Treg group, the MDSC group, the granulocytesgroup, the M2 signature group, the Th2 signature group, the protumorcytokines group, and the complement inhibition group; determining afirst set of visual characteristics for a first plurality of graphicaluser interface (GUI) elements using the gene group expression levelsdetermined for the first gene groups; determining a second set of visualcharacteristics for a second plurality of GUI elements using the genegroup expression levels determined for the second gene groups;generating a personalized GUI personalized to the subject, thegenerating comprising: generating a first GUI portion associated withcancer malignancy and containing the first plurality of GUI elementshaving the determined first set of visual characteristics; andgenerating a second GUI portion associated with cancer microenvironmentand containing the second plurality of GUI elements having thedetermined second set of visual characteristics; and presenting thegenerated personalized GUI to a user.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects and embodiments will be described with reference to thefollowing figures. The figures are not necessarily drawn to scale.

FIG. 1A is a graphical representation of an exemplary bioinformaticspipeline for determining tumor functional properties in a molecularfunctional profile (MF profile), in accordance with some embodiments ofthe technology described herein.

FIG. 1B is a graphical representation of tumor functional properties ina MF profile comprising 28 functional modules, in accordance with someembodiments of the technology described herein. The size of the modulescorrespond to their intensity rate. Colors reflect the module pro- oranti-cancer activity. Solid shades without cross-marking are assigned tothe modules that promote tumor growth, while shades of withcross-marking are assigned to those having anticancer activity. Themalignancy modules are collected in the Tumor Burden sector, which arelocated in the right top quarter of the graphical representation.

FIG. 1C shows an exemplary MF profile, in accordance with someembodiments of the technology described herein.

FIG. 2A is a block diagram of an illustrative environment 200 in whichsome embodiments of the technology described herein may be implemented.

FIG. 2B is a block diagram of an illustrative graphical user interface250 including patient data that may be presented to a user (e.g., adoctor), in accordance with some embodiments of the technology describedherein.

FIG. 2C is an illustrative example of the graphical user interface 250of FIG. 2B, in accordance with some embodiments of the technologydescribed herein.

FIG. 3 is a graphic illustrating different types of screens that may beshown to a user of the software program.

FIG. 4 is a screenshot of the user's account profile screen presented tothe user in response to the user logging into the software program.

FIG. 5 is a screenshot presenting the selected patient's informationprovided to the user in response to the user selecting the patient.

FIG. 6 is a screenshot presenting that the patient's tumor biopsysequencing data was downloaded (as shown in the lower right panel).

FIG. 7 is a screenshot presenting the selected patient's reportincluding information related to the patient's sequencing data, thepatient, and the patient's cancer.

FIG. 8 is a screenshot presenting information related to anti-PD1immunotherapy provided in response to selecting anti-PD1 immunotherapy(as shown by highlighting) in the immunotherapy biomarkers portion ofthe screen (as shown in the left panel).

FIG. 9 is a screenshot presenting selection of mutational burdenbiomarker by a user.

FIG. 10 is a screenshot presenting information relating to themutational burden biomarker (as shown in the middle panel) provided inresponse to the user selecting the mutational burden biomarker.

FIG. 11 is a screenshot presenting that the mutational status gene groupand neo-antigens load gene group in the MF profile are highlighted inresponse to the user selecting the mutational burden biomarker (as shownin highlighting).

FIG. 12 is a screenshot presenting that the T cells gene group in the MFprofile is highlighted in response to the user selecting the CD8 T cellbiomarker (as shown in highlighting).

FIG. 13 is a screenshot presenting that the checkpoint inhibition genegroup in the MF profile is highlighted in response to the user selectingthe PDL1 expression biomarker.

FIG. 14 is a screenshot presenting information related to sunitinibtherapy provided in response to selecting sunitinib (as shown byhighlighting) in the targeted therapy biomarkers portion of the screen(as shown in the left panel).

FIG. 15 is a screenshot presenting clinical trial data relating toanti-PD1 therapy effectivity in patients having stage IV metastaticmelanoma (as shown in the right panel) provided in response to the userselecting anti-PD1 immunotherapy (as shown in the left panel).

FIG. 16 is a screenshot presenting clinical trial data relating toanti-CTLA4 therapy effectivity in patients having stage IV metastaticmelanoma (as shown in the right panel) provided in response to the userselecting anti-CTLA4 immunotherapy (as shown in the left panel).

FIG. 17 is a screenshot presenting clinical trial data relating to theNCT01295827 clinical trial of anti-PD1 treatment (as shown in the middlepanel) provided in response to the user selecting the NCT01295827clinical trial (as shown in the right panel).

FIG. 18 is a screenshot presenting the treatment regimen of the selectedclinical data provided in response to the user minimizing the therapyclass description and drug description portions. The screen may alsopresent information relating to ongoing clinical trials (marked by theletter A).

FIG. 19 is a screenshot presenting a patient's MF profile (as shown inthe middle panel).

FIG. 20 is a screenshot presenting additional gene groups associatedwith the tumor properties gene group provided to the user in response toselecting the tumor properties gene group.

FIG. 21 is a screenshot presenting information relating to the tumorproliferation rate (as shown in the right panel) provided in response tothe user selecting the tumor proliferation rate gene group (as shown inhighlighting) in the MF profile.

FIG. 22 is a screenshot presenting information relating to the purity ofthe patient's tumor in the tumor purity portion (as shown in the lowerright panel) and information relating to the clonal evolution of thepatient's tumor in the tumor clones evolution portion (as shown in thelower right panel).

FIG. 23 is a screenshot presenting information relating to theanti-tumor immune environment (as shown in the left panel) provided inresponse to the user selecting the anti-tumor immune environment genegroup and information relating to the pro-tumor immune environment (asshown in the right panel) in response to the user selecting thepro-tumor immune environment gene group.

FIG. 24 is a screenshot presenting information relating to expression ofgenes that determine T cell activity within the tumor in the anti-tumormicroenvironment portion (as shown in the lower left panel) provided inresponse to the user selecting the T cell gene group in the MF profile(as shown by highlighting).

FIG. 25 is a screenshot presenting information relating to expression ofgenes that determine cancer associated fibroblast activity within thetumor in the pro-tumor microenvironment portion (as shown in the lowerright panel) provided in response to the user selecting the cancerassociated fibroblast gene group in the MF profile (as shown byhighlighting).

FIG. 26 is a screenshot presenting information relating to the number ofnon-malignant cells in the patient's tumor (as shown in the lower leftpanel) provided in response to the user selecting tumor infiltrate inthe anti-tumor immune environment portion (as shown in the upper leftpanel).

FIG. 27 is a screenshot presenting information relating to the TCRrepertoire in the patient's tumor (as shown in the lower right panel)provided in response to the user selecting tumor infiltrate in thepro-tumor immune environment portion (as shown in the upper rightpanel).

FIG. 28 is a screenshot showing a MF profile presenting twenty-eightgene groups is shown in (as shown in the middle panel).

FIG. 29 is a screenshot presenting the combo therapy portion (as shownin the right panel) provided to the user in response to selecting thecombinational therapy portion (as shown in the middle panel).

FIG. 30 is a screenshot presenting anti-PD1 therapy incorporated intothe combo therapy portion (as shown in the upper right panel).

FIG. 31 is a screenshot presenting information related to sunitinibtreatment in the therapy biological influence portion (as shown in thelower middle panel) in response to the user selecting sunitinib in thetargeted therapy biomarkers portion (as shown by highlighting).

FIG. 32 is a screenshot presenting sunitinib incorporation in the combotherapy portion in response to the user selecting sunitinib.

FIG. 33 is a screenshot presenting potential vaccine therapies such as apersonalized neo-antigenic vaccine and an off the shelf vaccine providedto the user in response to selecting vaccine in the immunotherapybiomarkers portion (as shown in the left panel).

FIG. 34 is a screenshot presenting information relating to treatmentwith a personalized neo-antigenic vaccine (as shown in the lower middlepanel) provided to the user in response to selecting a personalizedneo-antigenic vaccine (as shown by highlighting).

FIG. 35 is a screenshot presenting incorporation of a personalizedneo-antigenic vaccine in the combo therapy portion provided to the userin response to the user selecting the personalized neo-antigenicvaccine.

FIG. 36 is a screenshot presenting the personalized neo-antigenicvaccine therapy, anti-PD1 therapy, and sunitinib therapy in the combotherapy portion provided to the user in response to the userincorporating each of these therapies into the combo therapy portion.

FIG. 37 is a screenshot presenting an alert that substitution ofsunitinib therapy with vemurafenib therapy is recognized by the softwareas an inappropriate combination for the patient.

FIG. 38 is a block diagram of an illustrative computer system that maybe used in implementing some embodiments of the technology describedherein.

FIG. 39A is a flowchart of an illustrative process 3900 for identifyingan MF profile cluster with which to associate an MF profile for asubject, in accordance with some embodiments of the technology describedherein.

FIG. 39B is a flowchart of an illustrative process 3920 for generatingMF profile clusters using RNA expression data obtained from subjectshaving a particular type of cancer, in accordance with some embodimentsof the technology described herein.

FIG. 39C is a flowchart of an illustrative process 3940 for identifyingan MF profile cluster with which to associate an MF profile determinedfor a subject at least in part by determining the subject's expressionlevels for multiple gene groups, in accordance with some embodiments ofthe technology described herein.

FIG. 39D is a flowchart of an illustrative process 3960 for generatingMF profile clusters using RNA expression data obtained from subjectshaving a particular type of cancer, and associating a subject with oneof the generated MF clusters based on the subject's MF profile, inaccordance with some embodiments of the technology described herein.

FIG. 40A is a flowchart of an illustrative process 4000 for generatingan MF profile and generating an MF portrait for visualizing the MFprofile in a graphical user interface (GUI), in accordance with someembodiments of the technology described herein.

FIG. 40B is a flowchart of an illustrative process 4020 for generatingan MF profile by determining expression levels for multiple gene groupsand generating an MF portrait for visualizing the MF profile in agraphical user interface (GUI), in accordance with some embodiments ofthe technology described herein.

FIG. 41A shows a series of MF profiles of melanoma patients (n=45)chosen randomly, in accordance with some embodiments of the technologydescribed herein.

FIG. 41B shows data from an unsupervised dense subgraph network clusteranalysis of tumor functional processes calculated from RNA-Seq data ofpatient melanoma tumors (n=470 patients), in accordance with someembodiments of the technology described herein. The determined clusterswere labeled Types A-D (1^(st)-4^(th) MF profile clusters,respectively).

FIG. 41C is a graphical representation of a correlation-based graphnetwork of patients showing determined clusters, in accordance with someembodiments of the technology described herein. Each dot represents anindividual melanoma patient, who is connected to other patients with aweight corresponding to its correlation value. The size of the dotcorresponds to the vertex degree.

FIG. 41D shows data from a k-means clustering analysis of tumorfunctional processes calculated from RNA-Seq data of melanoma tumors(n=470 patient tumors), in accordance with some embodiments of thetechnology described herein. The determined clusters were labeled TypesA-D (1^(st)-4^(th) MF profile clusters, respectively).

FIG. 41E shows data from a cell composition analysis of melanoma tumorsgrouped into determined cluster Types A-D (1^(st)-4^(th) MF profileclusters, respectively) using MCP-counter and CIBERSORT, in accordancewith some embodiments of the technology described herein.

FIG. 41F shows data from a gene set enrichment analysis of melanomatumors grouped into determined cluster Types A-D (1^(st)-4^(th) MFprofile clusters, respectively), in accordance with some embodiments ofthe technology described herein.

FIG. 41G is a graphical representation of functional process intensityassociated with tumor growth (e.g., CAF, Angiogenesis, or Proliferationrate) or intratumoral immune infiltrate (e.g., effector cells orregulatory T cells (Tregs)) layered on cancers of determined clusterTypes A-D (1^(st)-4^(th) MF profile clusters, respectively), inaccordance with some embodiments of the technology described herein.

FIG. 41H shows data from a log(p-value) t-test difference in processactivity (enrichment score) between cancers of determined cluster TypesA-D (1^(st)-4^(th) MF profile clusters, respectively), in accordancewith some embodiments of the technology described herein.

FIG. 41I shows Kaplan-Meier survival curves for melanoma patients splitinto cohorts according to the their MF profile determined cluster types(Types A-D; which are equivalent to the 1^(st)-4^(th) types of portraitsdescribed herein, respectively) using unsupervised dense subgraphnetwork clustering, in accordance with some embodiments of thetechnology described herein.

FIG. 41J shows Kaplan-Meier survival curves for melanoma patients splitinto cohorts according to the their MF profile determined cluster types(Types A-D; which are equivalent to the 1^(st)-4^(th) types of portraitsdescribed herein, respectively) using k-means clustering, in accordancewith some embodiments of the technology described herein.

FIG. 41K shows data from a purity, mutational load and mutational statusanalysis of melanoma tumors grouped according to their determinedcluster Types A-D (which are equivalent to the 1^(st)-4^(th) types ofportraits described herein, respectively), in accordance with someembodiments of the technology described herein.

FIG. 42A shows a MF profile type A (first type) as determined inaccordance with some embodiments of the technology described herein.

FIG. 42B shows a MF profile type B (second type), as determined inaccordance with some embodiments of the technology described herein.

FIG. 42C shows a MF profile type C (third type), as determined inaccordance with some embodiments of the technology described herein.

FIG. 42D shows a MF profile type D (fourth type), as determined inaccordance with some embodiments of the technology described herein.

FIG. 43A shows data from a tSNE analysis over non-normalized processenrichment scores, in accordance with some embodiments of the technologydescribed herein. Each data point corresponds to an individual analyzedtumor sample. Different datasets (e.g., cancer types) are indicated byvarious grayscale intensities.

FIG. 43B shows data from a tSNE analysis over process enrichment scoresnormalized within specific cancer types, in accordance with someembodiments of the technology described herein. Each data pointcorresponds to an individual tumor sample analyzed. Different datasets(e.g., cancer types) are indicated by various grayscale intensities.

FIG. 43C shows data from an unsupervised dense subgraph network clusteranalysis of tumor functional processes calculated from RNA-Seq data ofdifferent patient tumors. The following cancers were analyzed using TCGAdata (listed n values indicate the numbers of individual patients):ACC—adrenocortical carcinoma (n=80), BLCA—bladder urothelial carcinoma(n=412), BRCA—breast invasive carcinoma (n=1100), CESC—cervical squamouscell carcinoma and endocervical adenocarcinoma (n=308), COAD—colonadenocarcinoma (n=461), ESCA—esophageal carcinoma (n=185), KIRC—kidneyrenal clear cell carcinoma (n=536), KIRP—kidney renal papillary cellcarcinoma (n=291), LIHC—liver hepatocellular carcinoma (n=377),LUAD—lung adenocarcinoma (n=521), LUSC—lung squamous cell carcinoma(n=510), OV—ovarian serous cystadenocarcinoma (n=586), PAAD—pancreaticadenocarcinoma (n=185), PRAD—prostate adenocarcinoma (n=498),READ—rectal adenocarcinoma (n=172), SKCM—skin cutaneous melanoma(n=470), STAD—stomach adenocarcinoma (n=445), THCA—thyroid carcinoma(n=507), UCEC—uterine corpus endometrial carcinoma (n=548),CHOL—Cholangiocarcinoma—(n=36).

FIG. 43D shows the frequency of determined cancer cluster Types A-D(1^(st)-4^(th) MF profile clusters, respectively) in patients havingdifferent malignant neoplasms, in accordance with some embodiments ofthe technology described herein.

FIG. 43E shows data from an unsupervised dense subgraph network clusteranalysis of tumor functional processes calculated from RNA-Seq data ofpatient having different malignant neoplasms, in accordance with someembodiments of the technology described herein. The determined clusterswere labeled Types A-D (1^(st)-4^(th) MF profile clusters,respectively).

FIG. 43F shows Kaplan-Meier survival curves for patients havingdifferent malignant neoplasms split into cohorts according to the theirdetermined cancer cluster Types A-D (1^(st)-4^(th) MF profile clusters,respectively), in accordance with some embodiments of the technologydescribed herein.

FIG. 44A shows data from a k-means clustering analysis of tumorfunctional processes calculated from RNA-Seq data for each cancersample, in accordance with some embodiments of the technology describedherein. The determined clusters were labeled Types A-D (1^(st)-4^(th) MFprofile clusters, respectively).

FIG. 44B shows data from a k-means clustering analysis of tumorfunctional processes calculated from RNA-Seq data for merged pan-cancertumors, in accordance with some embodiments of the technology describedherein. The determined clusters were labeled Types A-D (1^(st)-4^(th) MFprofile clusters, respectively).

FIG. 44C shows data from a log(p-value) t-test difference in processactivity enrichment scores between determined cancer cluster Types A-D(1^(st)-4^(th) MF profile clusters, respectively) for merged pan-cancertumors, in accordance with some embodiments of the technology describedherein.

FIG. 44D shows a heatmap of correlation between melanoma samples (n=470)and the 10,000 most expressed genes, in accordance with some embodimentsof the technology described herein. Pearson correlation matrices wereclustered using Euclidean distance measured by the complete linkagemethod. Dense clusters are highlighted in column bar.

FIG. 44E shows a heatmap of correlation between melanoma samples (n=470)and to 298 genes constituting the functional processes, in accordancewith some embodiments of the technology described herein.

FIG. 44F shows a heatmap of correlation between melanoma samples (n=470)and to 28 functional process scores, in accordance with some embodimentsof the technology described herein.

FIG. 44G shows a heatmap of correlation between 20 different carcinomatumors, in accordance with some embodiments of the technology describedherein. Panel (1) shows correlation with the 10,000 most expressedgenes; panel (2) shows correlation with 298 genes constituting thefunctional processes; and panel (3) shows correlation with the 28functional process scores. Pearson correlation matrices were clusteredusing Euclidean distance measured by the complete linkage method. Denseclusters are highlighted in column bar.

FIG. 45A shows data from an unsupervised dense subgraph network clusteranalysis of tumor functional processes calculated from RNA-Seq data ofpatient glioblastoma tumors (n=159) and glioma tumors (n=516), inaccordance with some embodiments of the technology described herein. Thedetermined clusters were labeled Types A-D (1^(st)-4^(th) MF profileclusters, respectively).

FIG. 45B shows data from a log(p-value) t-test difference in processactivity enrichment scores between brain tumors determined to fallwithin cluster Types A-D (1^(st)-4^(th) MF profile clusters,respectively), in accordance with some embodiments of the technologydescribed herein.

FIG. 45C shows data from an unsupervised dense subgraph network clusteranalysis of tumor functional processes calculated from RNA-Seq data ofpatient sarcoma tumors (n=261), in accordance with some embodiments ofthe technology described herein. The determined clusters were labeledTypes A-D (1^(st)-4^(th) MF profile clusters, respectively).

FIG. 45D shows data from a log(p-value) t-test difference in processactivity enrichment scores between sarcoma tumors determined to fallwithin cluster Types A-D (1^(st)-4^(th) MF profile clusters,respectively), in accordance with some embodiments of the technologydescribed herein.

FIG. 46A shows a heatmap showing processes of tumor MF profiles ofmelanoma patients treated with anti-CTLA4 therapy, in accordance withsome embodiments of the technology described herein. Annotation ofresponders and non-responders, MF profile classification of determinedcluster Types A-D (1^(st)-4^(th) MF profile clusters, respectively), andtotal number of mutations is shown above the heatmap. The average MFprofiles corresponding to patients from the heatmap and percentresponders (R) and non-responders (N) for patients having the indicatedtumor type are shown under the heatmap.

FIG. 46B shows a heatmap showing processes of tumor MF profiles ofmelanoma patients treated with anti-PD1 therapy, in accordance with someembodiments of the technology described herein.

FIG. 46C shows a heatmap showing processes of tumor MF profiles ofmelanoma patients treated with MAGE-A3 vaccine, in accordance with someembodiments of the technology described herein.

FIG. 46D shows a heatmap showing processes of tumor MF profiles of mCRCpatients from GSE5851 and HNSCC patients from GSE65021 treated withcetuximab, in accordance with some embodiments of the technologydescribed herein. EGFR expression status is also indicated.

FIG. 46E shows a heatmap showing processes of tumor MF profiles of ccRCCpatients treated with sunitinib, in accordance with some embodiments ofthe technology described herein.

FIG. 46F shows data of receiver operating characteristics for therapyresponse prediction based on MF profile type and AUC scores, inaccordance with some embodiments of the technology described herein.

FIG. 46G shows Kaplan-Meier survival curves for melanoma patientstreated with anti-CTLA4 therapy split into cohorts according to thetheir determined MF profile type (Types A-D; 1^(st)-4^(th) MF profileclusters, respectively), in accordance with some embodiments of thetechnology described herein.

FIG. 46H shows Kaplan-Meier survival curves for melanoma patientstreated with anti-PD1 therapy split into cohorts according to the theirdetermined MF profile type (Types A-D; 1^(st)-4^(th) MF profileclusters, respectively), in accordance with some embodiments of thetechnology described herein.

FIG. 46I shows Kaplan-Meier survival curves for sunitinib treatedpatients having tumors with a high proliferation rate or a lowproliferation rate, in accordance with some embodiments of thetechnology described herein.

FIG. 47A shows a graphical representation of melanoma patients (dots) ontwo-dimensional coordinates of T cells and Cancer Associated Fibroblastprocess intensity from MF profile (z-scores), in accordance with someembodiments of the technology described herein. MF profile type (TypesA-D; 1^(st)-4^(th) MF profile clusters, respectively) is indicated foreach patient. Dynamic changes in tumor MF profiles of five patients areshown by arrows. Larger dots indicate pre-treatment tumors.

FIG. 47B shows a heatmap showing processes of determined MF profile type(Types A-D; 1^(st)-4^(th) MF profile clusters, respectively) formelanoma patients before and after treatment with anti-PD1 therapy, inaccordance with some embodiments of the technology described herein.Annotation of responders and non-responders, MF profile classificationand total number of mutations is shown above the heatmap. Pre-treatmentMF profiles for each patient are shown under the heatmap.

FIG. 47C shows a graph of receiver operating characteristics for therapyresponse prediction based on tumor classification before treatment, inaccordance with some embodiments of the technology described herein.

FIG. 48A shows an exemplary MF profile useful for designing acombination therapy, in accordance with some embodiments of thetechnology described herein.

FIG. 48B shows an exemplary MF profile type B useful for designing acombination therapy, in accordance with some embodiments of thetechnology described herein.

FIG. 48C shows an exemplary MF profile type C useful for designing acombination therapy, in accordance with some embodiments of thetechnology described herein.

FIG. 48D shows an exemplary MF profile type D useful for designing acombination therapy, in accordance with some embodiments of thetechnology described herein.

FIG. 49A is a graphical representation of an exemplary MF profile having28 functional processes, in accordance with some embodiments of thetechnology described herein.

FIG. 49B is a graphical representation showing a visualization of a MFprofile having 19 functional processes, in accordance with someembodiments of the technology described herein.

FIG. 49C is a graphical representation showing a visualization of a MFprofile having 5 functional processes, in accordance with someembodiments of the technology described herein.

FIG. 50A shows data from a Pearson correlation analysis of functionalmodules which form the basis for the tumor MF profiles, in accordancewith some embodiments of the technology described herein.

FIG. 50B shows data from a Pearson correlation analysis of Effectorcells functional activity module with NK cells, Checkpoint inhibition,MHC class II and Metastasis modules, in accordance with some embodimentsof the technology described herein.

FIG. 50C shows a graph showing SKCM and pan-cancer graph nodeconnectivity percent (%) on different edge weight thresholds, inaccordance with some embodiments of the technology described herein. Thesolid line shows 1% node connectivity.

DETAILED DESCRIPTION

Recent advances in personalized genomic sequencing and cancer genomicsequencing technologies have made it possible to obtain patient-specificinformation about cancer cells (e.g., tumor cells) and cancermicroenvironments from one or more biological samples obtained fromindividual patients. This information can be used to characterize thetype or types of cancer a patient or subject has and, potentially,select one or more effective therapies for the patient. This informationmay also be used to determine how a patient is responding over time to atreatment and, if necessary, to select a new therapy or therapies forthe patient as necessary. This information may also be used to determinewhether a patient should be included or excluded from participating in aclinical trial.

The inventors have recognized and appreciated that many different typesof cancer including, but not limited to melanoma, sarcoma, andglioblastoma, may be characterized as or classified into one of fourmolecular function (MF) profiles, herein identified as first MF profile(1^(st) MF profile), second MF profile (2^(nd) MF profile), third MFprofile (3^(rd) MF profile), and fourth MF profile (4^(th) MF profile).

First MF profile cancers may also be described as“inflamed/vascularized” and/or “inflamed/fibroblast-enriched”; Second MFprofile cancers may also be described as “inflamed/non-vascularized”and/or “inflamed/non-fibroblast-enriched”; Third MF profile cancers mayalso be described as “non-inflamed/vascularized” and/or“non-inflamed/fibroblast-enriched”; and Fourth MF profile cancers mayalso be described as “non-inflamed/non-vascularized”and/or“non-inflamed/non-fibroblast-enriched” and/or “immune desert.” Suchcharacteristics of MF clusters may be calculated in a number of ways.

As used herein, “inflamed” refers to the level of compositions andprocesses related to inflammation in a cancer (e.g., a tumor). In someembodiments, inflamed cancers (e.g., tumors) are highly infiltrated byimmune cells, and are highly active with regard to antigen presentationand T-cell activation. In some embodiments, inflamed cancers (e.g.,tumors) may have an NK cell and/or a T cell z score of, for example, atleast 0.60, at least 0.65, at least 0.70, at least 0.75, at least 0.80,at least 0.85, at least 0.90, at least 0.91, at least 0.92, at least0.93, at least 0.94, at least 0.95, at least 0.96, at least 0.97, atleast 0.98, or at least 0.99. In some embodiments, inflamed cancers(e.g., tumors) may have an NK cell and/or a T cell z score of, forexample, not less than 0.60, not less than 0.65, not less than 0.70, notless than 0.75, not less than 0.80, not less than 0.85, not less than0.90, not less than 0.91, not less than 0.92, not less than 0.93, notless than 0.94, not less than 0.95, not less than 0.96, not less than0.97, not less than 0.98, or not less than 0.99. In some embodiments,non-inflamed tumors are poorly infiltrated by immune cells, and have lowactivity with regard to antigen presentation and T-cell activation. Insome embodiments, non-inflamed cancers (e.g., tumors) may have an NKcell and/or a T cell z score of, for example, less than −0.20, less than−0.25, less than −0.30, less than −0.35, less than −0.40, less than−0.45, less than −0.50, less than −0.55, less than −0.60, less than−0.65, less than −0.70, less than −0.75, less than −0.80, less than−0.85, less than −0.90, less than −0.91, less than −0.92, less than−0.93, less than −0.94, less than −0.95, less than −0.96, less than−0.97, less than −0.98, or less than −0.99. In some embodiments,non-inflamed cancers (e.g., tumors) may have an NK cell and/or a T cellz score of, for example, not more than −0.20, not more than −0.25, notmore than −0.30, not more than −0.35, not more than −0.40, not more than−0.45, not more than −0.50, not more than −0.55, not more than −0.60,not more than −0.65, not more than −0.70, not more than −0.75, not morethan −0.80, not more than −0.85, not more than −0.90, not more than−0.91, not more than −0.92, not more than −0.93, not more than −0.94,not more than −0.95, not more than −0.96, not more than −0.97, not morethan −0.98, or not more than −0.99.

As used herein, “vascularized” refers to the formation of blood vesselsin a cancer (e.g., a tumor). In some embodiments, vascularized cancers(e.g., tumors) comprise high levels of cellular compositions and processrelated to blood vessel formation. In some embodiments, vascularizedcancers (e.g., tumors) may have an angiogenesis z score of, for example,at least 0.60, at least 0.65, at least 0.70, at least 0.75, at least0.80, at least 0.85, at least 0.90, at least 0.91, at least 0.92, atleast 0.93, at least 0.94, at least 0.95, at least 0.96, at least 0.97,at least 0.98, or at least 0.99. In some embodiments, vascularizedcancers (e.g., tumors) may have an NK cell and/or a T cell z score of,for example, not less than 0.60, not less than 0.65, not less than 0.70,not less than 0.75, not less than 0.80, not less than 0.85, not lessthan 0.90, not less than 0.91, not less than 0.92, not less than 0.93,not less than 0.94, not less than 0.95, not less than 0.96, not lessthan 0.97, not less than 0.98, or not less than 0.99. In someembodiments, non-vascularized cancers (e.g., tumors) comprise few or nocompositions and processes related to blood vessel formation. In someembodiments, non-vascularized cancers (e.g., tumors) may have anangiogenesis z score of, for example, less than −0.20, less than −0.25,less than −0.30, less than −0.35, less than −0.40, less than −0.45, lessthan −0.50, less than −0.55, less than −0.60, less than −0.65, less than−0.70, less than −0.75, less than −0.80, less than −0.85, less than−0.90, less than −0.91, less than −0.92, less than −0.93, less than−0.94, less than −0.95, less than −0.96, less than −0.97, less than−0.98, or less than −0.99. In some embodiments, non-vascularized cancers(e.g., tumors) may have an angiogenesis z score of, for example, notmore than −0.20, not more than −0.25, not more than −0.30, not more than−0.35, not more than −0.40, not more than −0.45, not more than −0.50,not more than −0.55, not more than −0.60, not more than −0.65, not morethan −0.70, not more than −0.75, not more than −0.80, not more than−0.85, not more than −0.90, not more than −0.91, not more than −0.92,not more than −0.93, not more than −0.94, not more than −0.95, not morethan −0.96, not more than −0.97, not more than −0.98, or not more than−0.99.

As used herein, “fibroblast enriched” refers to the level or amount offibroblasts in a cancer (e.g., a tumor). In some embodiments, fibroblastenriched tumors comprise high levels of fibroblast cells. In someembodiments, fibroblast enriched cancers (e.g., tumors) may have afibroblast (cancer associated fibroblast) z score of, for example, atleast 0.60, at least 0.65, at least 0.70, at least 0.75, at least 0.80,at least 0.85, at least 0.90, at least 0.91, at least 0.92, at least0.93, at least 0.94, at least 0.95, at least 0.96, at least 0.97, atleast 0.98, or at least 0.99. In some embodiments, fibroblast enrichedcancers (e.g., tumors) may have an NK cell and/or a T cell z score of,for example, not less than 0.60, not less than 0.65, not less than 0.70,not less than 0.75, not less than 0.80, not less than 0.85, not lessthan 0.90, not less than 0.91, not less than 0.92, not less than 0.93,not less than 0.94, not less than 0.95, not less than 0.96, not lessthan 0.97, not less than 0.98, or not less than 0.99. In someembodiments, non-fibroblast-enriched cancers (e.g., tumors) comprise fewor no fibroblast cells. In some embodiments, non-fibroblast-enrichedcancers (e.g., tumors) may have a fibroblast (cancer associatedfibroblast) z score of, for example, less than −0.20, less than −0.25,less than −0.30, less than −0.35, less than −0.40, less than −0.45, lessthan −0.50, less than −0.55, less than −0.60, less than −0.65, less than−0.70, less than −0.75, less than −0.80, less than −0.85, less than−0.90, less than −0.91, less than −0.92, less than −0.93, less than−0.94, less than −0.95, less than −0.96, less than −0.97, less than−0.98, or less than −0.99. In some embodiments, non-fibroblast-enrichedcancers (e.g., tumors) may have a fibroblast (cancer associatedfibroblast) z score of, for example, not more than −0.20, not more than−0.25, not more than −0.30, not more than −0.35, not more than −0.40,not more than −0.45, not more than −0.50, not more than −0.55, not morethan −0.60, not more than −0.65, not more than −0.70, not more than−0.75, not more than −0.80, not more than −0.85, not more than −0.90,not more than −0.91, not more than −0.92, not more than −0.93, not morethan −0.94, not more than −0.95, not more than −0.96, not more than−0.97, not more than −0.98, or not more than −0.99.

Each subject biological sample may be assigned to one of four predefinedMF profile clusters using a k-nearest neighbors classifier. Theclassifier may be trained on the data by which the MF profile clustersare defined and on their corresponding labels. Sample vectors for thek-nearest neighbors classifier may be found in Table 1, below. Theclassifier may then predict the type of MF profile (MF profile cluster)for the subject sample utilizing its relative processes intensityvalues. Relative processes intensity values may be calculated asZ-values (arguments of the standard normal distribution over trainingset of samples) of ssGSEA algorithm outputs inferred from the RNAsequence data from the subject sample as described herein.

TABLE 1 Sample vectors for the k-nearest neighbors classifier (z-scores). MF profile type First Second Third Fourth Angiogenesis0.727815 −0.5907 0.71314 −0.42704 Cancer Associated Fibroblasts 0.596986−0.4871 0.82218 −0.49264 Receptor_tyrosine_kinases 0.370197 −0.43660.75614 −0.33472 NK_cells 0.624648 0.75725 −0.3987 −0.89695Checkpoint_inhibition 0.671491 0.74881 −0.3928 −0.92683 Effector_cells0.652837 0.77783 −0.3953 −0.93822 T_cells 0.701067 0.74591 −0.3827−0.9518 Proliferation_rate −0.44244 0.10307 −0.457 0.509505

The identification and classification of 1^(st)-4^(th) MF profilecluster types as described herein were not known in the art, and suchclassifications provide more precise diagnoses that might not be seen bythe use of any single marker or less complex combination of elements.The methods, systems, and graphical user interfaces (GUIs) based on suchclassifications described herein are newly available and no previouslydescribed techniques or methods existed to perform the elements of thesetechniques. Further, the four molecular function (MF) profiles were notknown previously to exist and there could therefore be no motivation inthe art to define these cancer types. Additionally, the types ofanalyses described herein would have been considered too involved,costly, and/or time consuming to perform without understanding thepotential benefits that could be derived from such complex analysesbased on the multiplicity and mutability of the involved factors.

The inventors have recognized and appreciated that several of theelements described herein add something more than what is wellunderstood, routine, or conventional activity proposed by others in thefield. These meaningful non-routine steps result in the improvementsseen in the methods, systems, and GUIs described herein and include, butare not limited to: the analysis of gene expression levels and genegroup expression levels for both cancer malignancy and cancermicroenvironment; the combination(s) of specific genes used in the genegroups (or modules) provided herein; the recognition that many differentcancers can be classified such that they are identifiable as one of1^(st)-4^(th) MF profile cancer types; technical improvements inanalyses that allow for more precise identification of cancers andresulting improvements in outcome for the patient; the creation ofimproved graphical user interfaces to aid in the analysis of anindividual patient's cancer into cancer 1^(st)-4^(th) MF profile cancertypes; the specification of treatments for individual patients based onthe identified classification of one or more cancers in the patient(i.e., 1^(st)-4^(th) MF profile cancer types) and/or additionalinformation about the patient or the patient's cancer.

Therefore, aspects of the present disclosure relate to methods andcompositions for characterizing one or more cancers (e.g., tumors) of orin a patient. In some embodiments, characterizing a cancer (e.g., atumor) comprises determining differentially expressed genes in a samplefrom a subject (e.g., a patient) having a cancer (e.g., a tumor). Insome embodiments, characterizing a cancer (e.g., a tumor) comprisesdetermining whether one or more genes are mutated in a sample from asubject having a cancer (e.g., a tumor). In certain embodiments,characterizing a cancer (e.g., a tumor) comprises identifying the cancer(e.g., a tumor) as a specific subtype of cancer selected from a 1^(st)MF profile cancer type (inflamed/vascularized and/or inflamed/fibroblastenriched); a 2^(nd) MF profile cancer type (inflamed/non-vascularizedand/or inflamed/non-fibroblast enriched); a 3^(rd) MF profile cancertype (non-inflamed/vascularized and/or non-inflamed/fibroblastenriched); and a 4^(th) MF profile cancer type(non-inflamed/non-vascularized and/or non-inflamed/non-fibroblastenriched; also identified herein as “immune desert”).

Such methods and compositions may be useful for clinical purposesincluding, for example, selecting a treatment, monitoring cancerprogression, assessing the efficacy of a treatment against a cancer,evaluating suitability of a patient for participating in a clinicaltrial, or determining a course of treatment for a subject (e.g., apatient).

The methods and compositions described herein may also be useful fornon-clinical applications including (as a non-limiting example) researchpurposes such as, e.g., studying the mechanism of cancer developmentand/or biological pathways and/or biological processes involved incancer, and developing new therapies for cancer based on such studies.

Further, systems which present this information in a comprehensive anduseable format will be needed to facilitate treatment of patients withsuch conditions. Therefore, provided herein are models and systems ofcancer-immunity interrelationships for a particular patient that resultin a profile designed to concisely and clearly describe importantcharacteristics of cancerous cells (e.g., tumor cells) of the patient(referred to herein as, for example, “cancer malignancy”), as well asall the key processes in the cancer (e.g., tumor) microenvironment(discussed herein as, for example, “cancer microenvironment”).

Such a model may take into consideration the full spectrum ofnon-malignant components in the cancer microenvironment, includingfibroblasts and extracellular matrices, the network of blood andlymphatic vessels, tissue macrophages, dendritic and mast cells,different kinds of leukocytes/lymphocytes migrated to or proliferatingwithin tumor, as well as intrinsic properties of malignant cells.

Certain aspects of the described model or system present the cellularcomposition of the cancerous cells (e.g., the tumor), while otheraspects reflect the intensity of processes of the cancerous (e.g., thetumor) cells of the biological sample and/or patient. The presence andnumber of any cell type is an important but insufficient parameterbecause it is also necessary to understand how these cells functionwithin the processes that make up the cancer (e.g., the tumor). The sizeof particular functional modules including, e.g., the intensity ofprocesses ongoing in these modules, actually comprises bothconcentration and functional activity of the cell type. Therefore, acancer (e.g., a tumor) “profile” that comprises a set of functionalmodules with an estimate of their intensity implicitly reflects thecontent of the different cell types within the cancer (e.g., the tumor).

Therefore, in some embodiments the model described herein enables thestudy of the structural-functional composition of a particular patient'stumor and/or cancerous cells and also allows the comparison of the sameacross different patients and groups of patients. As a non-limitingexample, the described model has been used to compare human skincutaneous melanoma (SKCM) tumors from 470 melanoma patients. Fourgeneral types of tumors were revealed (described here as tumor types1^(st) MF profile type, 2^(nd) MF profile type, 3^(rd) MF profile type,and 4^(th) MF profile type) pertaining to 22%, 28%, 24%, and 24% ofmelanoma patients, respectively (representing 98% of total patients).Tumor types 1^(st) MF profile type and 2^(nd) MF profile type arecharacterized by excessive infiltration with cells of the immune system(so-called “inflamed” or “hot” tumors), and 3^(rd) MF profile type and4^(th) MF profile type are considered poorly infiltrated (so-called“non-inflamed” or “cold” tumors), meaning they have no obvious signs ofinflammation or recruitment of immune cells.

Generally, techniques described herein provide for improvements overconventional computer-implemented techniques for analysis of medicaldata such as evaluation of expression data (e.g., RNA expression data)and determining whether one or more therapies (e.g., targeted therapiesand/or immunotherapies) will be effective in treating the subject.Additionally, some embodiments of the technology provided herein aredirected to graphical user interfaces that present oncological data in anew way which is compact and highly informative. These graphical userinterfaces not only reduce the cognitive load on users working withthem, but may serve to reduce clinician errors and improve thefunctionality of a computer by providing all needed information in asingle interactive interface. This eliminates the need for a clinicianto consult different sources of information (e.g., view multipledifferent webpages, use multiple different application programs, etc.),which would otherwise place an additional burden on the processing,memory, and communications resources of the computer(s) used by theclinician.

As described herein, some embodiments relate to a software program forproviding information related to a patient's cancer to a user (e.g., anoncologist or other doctor, a healthcare provider, a researcher, apatient, etc.). The software program may provide information about thepatient, e.g., the patient's age, overall status, diagnosis, andtreatment history.

In another aspect, the software program may provide information aboutthe patient's cancer, e.g., tumor histology, tumor purity, tumor cloneevolution, tumor cell composition, tumor cell infiltrate, geneexpression levels, gene mutations, the results of medical examinations(e.g., MRI results) and sequencing data (e.g., RNA sequencing dataand/or whole exome sequencing (WES) data).

In another aspect, the software program may provide information aboutpotential treatments (e.g., immunotherapies, targeted therapies, etc.)and information related to potential treatments, e.g., prognosticfactors, therapeutic efficacy, clinical trial efficacy, ongoing clinicaltrials, and relevant publications.

In another aspect, the software program may provide information aboutthe patient's biomarkers (e.g., genetic biomarkers, cellular biomarkers,and expression biomarkers) and information related to the patient'sbiomarkers (e.g., a description of the biomarker, how the biomarkervalue was calculated, the patient's particular biomarker value comparedto other patients, and related publications).

In yet another aspect, the software program may also allow the user tointeractively design a panel of sequencing results (e.g., resultsrelated to the sequences or levels of specified biomarkers or genes)specific to the patient and/or a combination therapy for the patient.

As used herein, the term “patient” means any mammal, including mice,rabbits, and humans. In one embodiment, the patient is a human ornon-human primate. The terms “individual” or “subject” may be usedinterchangeably with “patient.”

Obtaining Expression Data

Expression data (e.g., RNA expression data and/or whole exome sequencing(WES) data) as described herein may be obtained from a variety ofsources. In some embodiments, expression data may be obtained byanalyzing a biological sample from a patient. The biological sample maybe analyzed prior to performance of the techniques described hereinincluding the techniques for generating MF clusters, associating apatient's MF profile with one of the MF clusters, and generating an MFportrait from a patient's MF profile to provide a visualization for theMF profile. In some such embodiments, data obtained from the biologicalsample may be stored (e.g., in a database) and accessed duringperformance of the techniques described herein. In some embodiments,expression data is obtained from a database containing expression datafor at least one patient.

Biological Samples

Any of the methods, systems, assays, or other claimed elements may useor be used to analyze any biological sample from a subject (i.e., apatient or individual). In some embodiments, the biological sample maybe any sample from a subject known or suspected of having cancerouscells or pre-cancerous cells.

The biological sample may be from any source in the subject's bodyincluding, but not limited to, any fluid [such as blood (e.g., wholeblood, blood serum, or blood plasma), saliva, tears, synovial fluid,cerebrospinal fluid, pleural fluid, pericardial fluid, ascitic fluid,and/or urine], hair, skin (including portions of the epidermis, dermis,and/or hypodermis), oropharynx, laryngopharynx, esophagus, stomach,bronchus, salivary gland, tongue, oral cavity, nasal cavity, vaginalcavity, anal cavity, bone, bone marrow, brain, thymus, spleen, smallintestine, appendix, colon, rectum, anus, liver, biliary tract,pancreas, kidney, ureter, bladder, urethra, uterus, vagina, vulva,ovary, cervix, scrotum, penis, prostate, testicle, seminal vesicles,and/or any type of tissue (e.g., muscle tissue, epithelial tissue,connective tissue, or nervous tissue).

The biological sample may be any type of sample including, for example,a sample of a bodily fluid, one or more cells, a piece of tissue, orsome or all of an organ. In some embodiments, the sample may be from acancerous tissue or organ or a tissue or organ suspected of having oneor more cancerous cells. In some embodiments, the sample may be from ahealthy (e.g., non-cancerous) tissue or organ. In some embodiments, asample from a subject (e.g., a biopsy from a subject) may include bothhealthy and cancerous cells and/or tissue. In certain embodiments, onesample will be taken from a subject for analysis. In some embodiments,more than one (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, or more) samples may be taken from a subject foranalysis. In some embodiments, one sample from a subject will beanalyzed. In certain embodiments, more than one (e.g., 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more) samples maybe analyzed. If more than one sample from a subject is analyzed, thesamples may be procured at the same time (e.g., more than one sample maybe taken in the same procedure), or the samples may be taken atdifferent times (e.g., during a different procedure including aprocedure 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 days; 1, 2, 3, 4, 5, 6, 7, 8, 9,10 weeks; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 months, 1, 2, 3, 4, 5, 6, 7, 8,9, 10 years, or 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 decades after a firstprocedure). A second or subsequent sample may be taken or obtained fromthe same region (e.g., from the same tumor or area of tissue) or adifferent region (including, e.g., a different tumor). A second orsubsequent sample may be taken or obtained from the subject after one ormore treatments, and may be taken from the same region or a differentregion. As a non-limiting example, the second or subsequent sample maybe useful in determining whether the cancer in each sample has differentcharacteristics (e.g., in the case of samples taken from two physicallyseparate tumors in a patient) or whether the cancer has responded to oneor more treatments (e.g., in the case of two or more samples from thesame tumor prior to and subsequent to a treatment).

Any of the biological samples described herein may be obtained from thesubject using any known technique. In some embodiments, the biologicalsample may be obtained from a surgical procedure (e.g., laparoscopicsurgery, microscopically controlled surgery, or endoscopy), bone marrowbiopsy, punch biopsy, endoscopic biopsy, or needle biopsy (e.g., afine-needle aspiration, core needle biopsy, vacuum-assisted biopsy, orimage-guided biopsy). In some embodiments, each of the at least onebiological samples is a bodily fluid sample, a cell sample, or a tissuebiopsy.

In some embodiments, one or more than one cell (i.e., a cell sample) isobtained from a subject using a scrape or brush method. The cell samplemay be obtained from any area in or from the body of a subjectincluding, for example, from one or more of the following areas: thecervix, esophagus, stomach, bronchus, or oral cavity. In someembodiments, one or more than one piece of tissue (e.g., a tissuebiopsy) from a subject may be used. In certain embodiments, the tissuebiopsy may comprise one or more than one (e.g., 2, 3, 4, 5, 6, 7, 8, 9,10, or more than 10) samples from one or more tumors or tissues known orsuspected of having cancerous cells.

Sample Analysis

Methods and compositions described herein are based, at least in part,on the identification and characterization of certain biologicalprocesses and/or molecular and cellular compositions that are presentwithin and/or surrounding the cancer (e.g., the tumor).

Biological processes within and/or surrounding cancer (e.g., a tumor)include, but are not limited to, angiogenesis, metastasis,proliferation, cell activation (e.g., T cell activation), tumorinvasion, immune response, cell signaling (e.g., HER2 signaling), andapoptosis.

Molecular and cellular compositions within and/or surrounding cancer(e.g., a tumor) include, but are not limited to, nucleic acids (e.g.,DNA and/or RNA), molecules (e.g., hormones), proteins (e.g., wild-typeand/or mutant proteins), and cells (e.g., malignant and/or non-malignantcells).

The cancer microenvironment, as used herein, refers to the molecular andcellular environment in which the cancer (e.g., a tumor) existsincluding, but not limited to, blood vessels that surround and/or areinternal to a tumor, immune cells, fibroblasts, bone marrow-derivedinflammatory cells, lymphocytes, signaling molecules, and theextracellular matrix (ECM).

The molecular and cellular composition and biological processes presentwithin and/or surrounding the tumor may be directed toward promotingcancer (e.g., tumor) growth and survival (e.g., pro-tumor) and/orinhibiting cancer (e.g., tumor) growth and survival (e.g., anti-tumor).

The cancer (e.g., tumor) microenvironment may comprise cellularcompositions and biological processes directed toward promoting cancer(e.g., tumor) growth and survival (e.g., pro-tumor microenvironment)and/or inhibiting cancer (e.g., tumor) growth and survival (e.g.,anti-tumor microenvironment). In some embodiments, the cancer (e.g.,tumor) microenvironment comprises a pro-cancer (e.g., tumor)microenvironment. In some embodiments, the cancer (e.g., tumor)microenvironment comprises an anti-cancer (e.g., tumor)microenvironment. In some embodiments, the cancer (e.g., tumor)microenvironment comprises a pro-cancer (e.g., tumor) microenvironmentand an anti-cancer (e.g., tumor) microenvironment. Any informationrelating to molecular and cellular compositions, and biologicalprocesses that are present within and/or surrounding cancer (e.g., atumor) may be used in methods and compositions for characterization ofcancers (e.g., tumors) as described herein. In some embodiments, cancer(e.g., a tumor) may be characterized based on gene group expressionlevel (e.g., on gene group RNA expression level). In some embodiments,cancer (e.g., a tumor) is characterized based on protein expression. Insome embodiments, cancer (e.g., a tumor) is characterized based onabsence or presence of at least one mutation (e.g., mutational load). Insome embodiments, the mutational load is estimated from whole exomesequencing data (WES). In some embodiments, cancer (e.g., a tumor) ischaracterized based on histology. In some embodiments, cancer (e.g., atumor) is characterized based on tumor purity. Tumor purity may bedetermined using any means known in the art including, but not limitedto, cell sorting-based technology (e.g., Fluorescent-Activated CellSorting (FACS)). In some embodiments, tumor purity is determined fromwhole exome sequencing (WES) data of paired tumor and non-cancerous(e.g., normal) tissue. In some embodiments, cancer (e.g., a tumor) ischaracterized based on the number of neoantigens. The number ofneoantigens may be determined using any means known in the artincluding, but not limited to, the use of whole exome sequencing (WES)data of paired cancer (e.g., tumor) and non-cancerous tissues.

Methods and compositions for characterization of cancers as describedherein may be applied to any cancer (e.g., any tumor). Exemplary cancersinclude, but are not limited to, adrenocortical carcinoma, bladderurothelial carcinoma, breast invasive carcinoma, cervical squamous cellcarcinoma, endocervical adenocarcinoma, colon adenocarcinoma, esophagealcarcinoma, kidney renal clear cell carcinoma, kidney renal papillarycell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma,lung squamous cell carcinoma, ovarian serous cystadenocarcinoma,pancreatic adenocarcinoma, prostate adenocarcinoma, rectaladenocarcinoma, skin cutaneous melanoma, stomach adenocarcinoma, thyroidcarcinoma, uterine corpus endometrial carcinoma, and cholangiocarcinoma.

In one embodiment, cancers of any type (including all the types ofcancer listed herein) may be classified as being 1^(st) MF profile type(inflamed/vascularized and/or inflamed/fibroblast enriched), 2^(nd) MFprofile type (inflamed/non-vascularized and/or inflamed/non-fibroblastenriched), 3^(rd) MF profile type (non-inflamed/vascularized and/ornon-inflamed/fibroblast enriched), or 4^(th) MF profile type(non-inflamed/non-vascularized and/or non-inflamed/non-fibroblastenriched) cancers (e.g., tumors).

Expression Data

Expression data (e.g., indicating expression levels) for a plurality ofgenes may be used for any of the methods or compositions describedherein. The number of genes which may be examined may be up to andinclusive of all the genes of the subject. In some embodiments,expression levels may be examined for all of the genes of a subject. Asa non-limiting example, four or more, five or more, six or more, sevenor more, eight or more, nine or more, ten or more, eleven or more,twelve or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 ormore, 18 or more, 19 or more, 20 or more, 21 or more, 22 or more, 23 ormore, 24 or more, 25 or more, 26 or more, 27 or more, 28 or more, 29 ormore, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 ormore, 90 or more, 100 or more, 125 or more, 150 or more, 175 or more,200 or more, 225 or more, 250 or more, 275 or more, or 300 or more genesmay be used for any evaluation described herein. As another set ofnon-limiting examples, at least four, at least five, at least six, atleast seven, at least eight, at least nine, at least ten, at leasteleven, at least twelve, at least 13, at least 14, at least 15, at least16, at least 17, at least 18, at least 19, at least 20, at least 21, atleast 22, at least 23, at least 24, at least 25, at least 26, at least27, at least 28, at least 29, at least 30, at least 40, at least 50, atleast 60, at least 70, at least 80, at least 90, at least 100, at least125, at least 150, at least 175, at least 200, at least 225, at least250, at least 275, or at least 300 genes may be used for any evaluationdescribed herein. In some embodiments, at least two, at least three, atleast four, at least five, at least six, at least seven, at least eight,at least nine, at least ten, at least eleven, at least twelve, at least13, at least 14, at least 15, at least 16, at least 17, at least 18, atleast 19, at least 20, at least 21, at least 22, at least 23, at least24, at least 25, at least 26, at least 27, at least 28, at least 29, atleast 30, at least 40, at least 50, at least 60, at least 70, at least80, at least 90, at least 100, at least 125, at least 150, at least 175,at least 200, at least 225, at least 250, at least 275, or at least 300genes may be examined for each gene group or module evaluation describedherein. In some embodiments, up to 50 modules (e.g., up to two, up tothree, up to four, up to five, up to six, up to seven, up to eight, upto nine, up to ten, up to eleven, up to twelve, up to 13, up to 14, upto 15, up to 16, up to 17, up to 18, up to 19, up to 20, up to 21, up to22, up to 23, up to 24, up to 25, up to 26, up to 27, up to 28, up to29, up to 30, up to 31, up to 32, up to 33, up to 34, up to 35, up to36, up to 37, up to 38, up to 39, up to 40, up to 41, up to 42, up to43, up to 44, up to 45, up to 46, up to 47, up to 48, up to 49, or up to50) modules or gene groups may be used for any evaluation describedherein.

Any method may be used on a sample from a subject in order to acquireexpression data (e.g., indicating expression levels) for the pluralityof genes. As a set of non-limiting examples, the expression data may beRNA expression data, DNA expression data, or protein expression data.

DNA expression data, in some embodiments, refers to a level of DNA in asample from a subject. The level of DNA in a sample from a subjecthaving cancer may be elevated compared to the level of DNA in a samplefrom a subject not having cancer, e.g., a gene duplication in a cancerpatient's sample. The level of DNA in a sample from a subject havingcancer may be reduced compared to the level of DNA in a sample from asubject not having cancer, e.g., a gene deletion in a cancer patient'ssample.

DNA expression data, in some embodiments, refers to data for DNA (orgene) expressed in a sample, for example, sequencing data for a genethat is expressed in a patient's sample. Such data may be useful, insome embodiments, to determine whether the patient has one or moremutations associated with a particular cancer.

RNA expression data may be acquired using any method known in the artincluding, but not limited to: whole transcriptome sequencing, total RNAsequencing, mRNA sequencing, targeted RNA sequencing, small RNAsequencing, ribosome profiling, RNA exome capture sequencing, and/ordeep RNA sequencing. DNA expression data may be acquired using anymethod known in the art including any known method of DNA sequencing.For example, DNA sequencing may be used to identify one or moremutations in the DNA of a subject. Any technique used in the art tosequence DNA may be used with the methods and compositions describedherein. As a set of non-limiting examples, the DNA may be sequencedthrough single-molecule real-time sequencing, ion torrent sequencing,pyrosequencing, sequencing by synthesis, sequencing by ligation (SOLiDsequencing), nanopore sequencing, or Sanger sequencing (chaintermination sequencing). Protein expression data may be acquired usingany method known in the art including, but not limited to: N-terminalamino acid analysis, C-terminal amino acid analysis, Edman degradation(including though use of a machine such as a protein sequenator), ormass spectrometry.

In some embodiments, the expression data comprises whole exomesequencing (WES) data. In some embodiments, the expression datacomprises whole genome sequencing (WGS) data. In some embodiments, theexpression data comprises next-generation sequencing (NGS) data. In someembodiments, the expression data comprises microarray data.

In some embodiments, expression data is used to determine gene groupexpression levels. In some embodiments, the gene group expression levelsare calculated as a gene set enrichment analysis (GSEA) score for thegene group. In some embodiments, GSEA comprises calculating anenrichment score (ES), assessing ES significance, adjusting ES formultiple hypothesis testing, and weighting each gene. In someembodiments, each gene is weighted equally. In some embodiments, eachgene is weighted according to their association with a phenotype.

In some embodiments, calculating an ES comprises ranking genes by theirexpression difference, calculating cumulative sum over ranked genes, andrecording maximum deviation from zero as ES. In some embodiments,calculating cumulative sum over ranked genes comprises an increase insum when a gene is present in a gene group and a decrease in sum when agene is absent from gene group. In some embodiments, magnitude ofincrement depends on correlation of a gene and a phenotype.

In some embodiments, assessing ES significance comprises permutatingphenotype labels. In some embodiments, assessing ES significancecomprises calculating ES for permutated data. In some embodiments,assessing ES significance comprises comparing ES for non-permutated datato ES for permutated data.

In some embodiments, adjusting ES for multiple hypothesis testingcomprises determining a normalized enrichment score (NES). In someembodiments, adjusting ES for multiple hypothesis testing determining afalse discovery rate (FDR) for the NES. In some embodiments, determiningFDR comprises comparing tail of the observed and null distributions forthe NES. In some embodiments, GSEA score is calculated at least once. Insome embodiments, GSEA score is calculated at least twice. In someembodiments, GSEA score is calculated once for positively scoring genegroups and once for negatively score gene groups.

Datasets

Any dataset containing expression data may be used to generate MFprofiles as described herein. In some embodiments, expression data maybe obtained from one or more databases and/or any other suitableelectronic repository of data. Examples of databases include, but arenot limited to, CGP (Cancer Genome Project), CPTAC (Clinical ProteomicTumor Analysis Consortium), ICGC (International Cancer GenomeConsortium), and TCGA (The Cancer Genome Atlas). In some embodiments,expression data may be obtained from data associated with a clinicaltrial. In some embodiments, expression data may be predicted inassociation with a clinical trial based on one or more similar drugs(e.g., drugs of a similar class such as PD-1 inhibitors). In someembodiments, expression data may be obtained from a hospital database.In some embodiments, expression data may be obtained from a commercialsequencing supplier. In some embodiments, expression data may beobtained from a subject (e.g., a patient) and/or a subject's (e.g., apatient's) relative, guardian, or caretaker.

Assays

Any of the biological samples described herein can be used for obtainingexpression data using conventional assays or those described herein.Expression data, in some embodiments, includes gene expression levels.Gene expression levels may be detected by detecting a product of geneexpression such as mRNA and/or protein.

In some embodiments, gene expression levels are determined by detectinga level of a protein in a sample and/or by detecting a level of activityof a protein in a sample. As used herein, the terms “determining” or“detecting” may include assessing the presence, absence, quantity and/oramount (which can be an effective amount) of a substance within asample, including the derivation of qualitative or quantitativeconcentration levels of such substances, or otherwise evaluating thevalues and/or categorization of such substances in a sample from asubject.

The level of a protein may be measured using an immunoassay. Examples ofimmunoassays include any known assay (without limitation), and mayinclude any of the following: immunoblotting assay (e.g., Western blot),immunohistochemical analysis, flow cytometry assay, immunofluorescenceassay (IF), enzyme linked immunosorbent assays (ELISAs) (e.g., sandwichELISAs), radioimmunoas says, electrochemiluminescence-based detectionassays, magnetic immunoassays, lateral flow assays, and relatedtechniques. Additional suitable immunoassays for detecting a level of aprotein provided herein will be apparent to those of skill in the art.

Such immunoassays may involve the use of an agent (e.g., an antibody)specific to the target protein. An agent such as an antibody that“specifically binds” to a target protein is a term well understood inthe art, and methods to determine such specific binding are also wellknown in the art. An antibody is said to exhibit “specific binding” ifit reacts or associates more frequently, more rapidly, with greaterduration and/or with greater affinity with a particular target proteinthan it does with alternative proteins. It is also understood by readingthis definition that, for example, an antibody that specifically bindsto a first target peptide may or may not specifically or preferentiallybind to a second target peptide. As such, “specific binding” or“preferential binding” does not necessarily require (although it caninclude) exclusive binding. Generally, but not necessarily, reference tobinding means preferential binding. In some examples, an antibody that“specifically binds” to a target peptide or an epitope thereof may notbind to other peptides or other epitopes in the same antigen. In someembodiments, a sample may be contacted, simultaneously or sequentially,with more than one binding agent that binds different proteins (e.g.,multiplexed analysis).

As used herein, the term “antibody” refers to a protein that includes atleast one immunoglobulin variable domain or immunoglobulin variabledomain sequence. For example, an antibody can include a heavy (H) chainvariable region (abbreviated herein as VH), and a light (L) chainvariable region (abbreviated herein as VL). In another example, anantibody includes two heavy (H) chain variable regions and two light (L)chain variable regions. The term “antibody” encompasses antigen-bindingfragments of antibodies (e.g., single chain antibodies, Fab and sFabfragments, F(ab′)2, Fd fragments, Fv fragments, scFv, and domainantibodies (dAb) fragments (de Wildt et al., Eur J Immunol. 1996;26(3):629-39.)) as well as complete antibodies. An antibody can have thestructural features of IgA, IgG, IgE, IgD, IgM (as well as subtypesthereof). Antibodies may be from any source including, but not limitedto, primate (human and non-human primate) and primatized (such ashumanized) antibodies.

In some embodiments, the antibodies as described herein can beconjugated to a detectable label and the binding of the detectionreagent to the peptide of interest can be determined based on theintensity of the signal released from the detectable label.Alternatively, a secondary antibody specific to the detection reagentcan be used. One or more antibodies may be coupled to a detectablelabel. Any suitable label known in the art can be used in the assaymethods described herein. In some embodiments, a detectable labelcomprises a fluorophore. As used herein, the term “fluorophore” (alsoreferred to as “fluorescent label” or “fluorescent dye”) refers tomoieties that absorb light energy at a defined excitation wavelength andemit light energy at a different wavelength. In some embodiments, adetection moiety is or comprises an enzyme. In some embodiments, anenzyme is one (e.g., β-galactosidase) that produces a colored productfrom a colorless substrate.

It will be apparent to those of skill in the art that this disclosure isnot limited to immunoassays. Detection assays that are not based on anantibody, such as mass spectrometry, are also useful for the detectionand/or quantification of a protein and/or a level of protein as providedherein. Assays that rely on a chromogenic substrate can also be usefulfor the detection and/or quantification of a protein and/or a level ofprotein as provided herein.

Alternatively, the level of nucleic acids encoding a gene in a samplecan be measured via a conventional method. In some embodiments,measuring the expression level of nucleic acid encoding the genecomprises measuring mRNA. In some embodiments, the expression level ofmRNA encoding a gene can be measured using real-time reversetranscriptase (RT) Q-PCR or a nucleic acid microarray. Methods to detectnucleic acid sequences include, but are not limited to, polymerase chainreaction (PCR), reverse transcriptase-PCR (RT-PCR), in situ PCR,quantitative PCR (Q-PCR), real-time quantitative PCR (RT Q-PCR), in situhybridization, Southern blot, Northern blot, sequence analysis,microarray analysis, detection of a reporter gene, or other DNA/RNAhybridization platforms.

In some embodiments, the level of nucleic acids encoding a gene in asample can be measured via a hybridization assay. In some embodiments,the hybridization assay comprises at least one binding partner. In someembodiments, the hybridization assay comprises at least oneoligonucleotide binding partner. In some embodiments, the hybridizationassay comprises at least one labeled oligonucleotide binding partner. Insome embodiments, the hybridization assay comprises at least one pair ofoligonucleotide binding partners. In some embodiments, the hybridizationassay comprises at least one pair of labeled oligonucleotide bindingpartners.

Any binding agent that specifically binds to a desired nucleic acid orprotein may be used in the methods and kits described herein to measurean expression level in a sample. In some embodiments, the binding agentis an antibody or an aptamer that specifically binds to a desiredprotein. In other embodiments, the binding agent may be one or moreoligonucleotides complementary to a nucleic acid or a portion thereof.In some embodiments, a sample may be contacted, simultaneously orsequentially, with more than one binding agent that binds differentproteins or different nucleic acids (e.g., multiplexed analysis).

To measure an expression level of a protein or nucleic acid, a samplecan be in contact with a binding agent under suitable conditions. Ingeneral, the term “contact” refers to an exposure of the binding agentwith the sample or cells collected therefrom for suitable periodsufficient for the formation of complexes between the binding agent andthe target protein or target nucleic acid in the sample, if any. In someembodiments, the contacting is performed by capillary action in which asample is moved across a surface of the support membrane.

In some embodiments, an assay may be performed in a low-throughputplatform, including single assay format. In some embodiments, an assaymay be performed in a high-throughput platform. Such high-throughputassays may comprise using a binding agent immobilized to a solid support(e.g., one or more chips). Methods for immobilizing a binding agent willdepend on factors such as the nature of the binding agent and thematerial of the solid support and may require particular buffers. Suchmethods will be evident to one of ordinary skill in the art.

Genes

The various genes recited herein are, in general, named using human genenaming conventions. The various genes, in some embodiments, aredescribed in publically available resources such as published journalarticles. The gene names may be correlated with additional information(including sequence information) through use of, for example, the NCBIGenBank® databases available at www <dot> ncbi <dot> nlm <dot> nih <dot>gov; the HUGO (Human Genome Organization) Gene Nomination Committee(HGNC) databases available at www <dot> genenames <dot> org; the DAVIDBioinformatics Resource available at www <dot> david <dot> ncifcrf <dot>gov. The gene names may also be correlated with additional informationthrough printed publications from the foregoing organizations, which areincorporated by reference herein for this purpose. It should beappreciated that a gene may encompass all variants of that gene. Fororganisms or subjects other than human subjects, correspondingspecific-specific genes may be used. Synonyms, equivalents, and closelyrelated genes (including genes from other organisms) may be identifiedusing similar databases including the NCBI GenBank® databases describedabove.

In some embodiments, gene MK167 may be identified as GenBank® Accessionnumber NM_002417.4 or NM_001145966.1; gene ESCO2 may be identified asGenBank® Accession number NM_001017420.2; gene CETN3 may be identifiedas GenBank® Accession number NM_001297765.1, NM_004365.3 orNM_001297768.1; gene CDK2 may be identified as GenBank® Accession numberNM_001798.4, NM_052827.3 or NM_001290230.1; gene CCND1 may be identifiedas GenBank® Accession number NM_053056.2; gene CCNE1 may be identifiedas GenBank® Accession number NM_001238.3, NM_001322259.1, NM_001322261.1or NM_001322262.1; gene AURKA may be identified as GenBank® Accessionnumber NM_198433.2, NM_003600.3, NM_198434.2, NM_198435.2, NM_198436.2,NM_198437.2, NM_001323303.1, NM_001323304.1, or NM_001323305.1; geneAURKB may be identified as GenBank® Accession number NM_004217.3,NM_001256834.2, NM_001284526.1, NM_001313950.1, NM_001313951.1,NM_001313952.1, NM_001313954.1, NM_001313953.2 or NM_001313955.1; geneCDK4 may be identified as GenBank® Accession number NM_000075.3; geneCDK6 may be identified as GenBank® Accession number NM_001145306.1; genePRC1 may be identified as GenBank® Accession number NM_199413.2 orNM_003981.3; gene E2F1 may be identified as GenBank® Accession numberNM_005225.2; gene MYBL2 may be identified as GenBank® Accession numberNM_002466.3 or NM_001278610.1; gene BUB1 may be identified as GenBank®Accession number NM_004336.4, NM_001278616.1, NM_001278617.1; gene PLK1may be identified as GenBank® Accession number NM_005030.5; gene CCNB1may be identified as GenBank® Accession number NM_031966.3,NM_001354845.1, NM_001354844.1; gene MCM2 may be identified as GenBank®Accession number NM_004526.3; gene MCM6 may be identified as GenBank®Accession number NM_005915.5; gene PIK3CA may be identified as GenBank®Accession number NM_006218.3; gene PIK3CB may be identified as GenBank®Accession number NM_006219.2 or NM_001256045.1; gene PIK3CG may beidentified as GenBank® Accession number NM_002649.3, NM_001282427.1 orNM_001282426.1; gene PIK3CD may be identified as GenBank® Accessionnumber NM_005026.4, NM_001350234.1, or NM_001350235.1; gene AKT1 may beidentified as GenBank® Accession number NM_005163.2, NM_001014431.1, orNM_001014432.1; gene MTOR may be identified as GenBank® Accession numberNM_004958.3; gene PTEN may be identified as GenBank® Accession numberNM_001304717.2, NM_000314.6 or NM_001304718.1; gene PRKCA may beidentified as GenBank® Accession number NM_002737.2; gene AKT2 may beidentified as GenBank® Accession number NM 001330511.1, NM_001243027.2,NM_001243028.2, NM_001626.5; gene AKT3 may be identified as GenBank®Accession number NM_005465.4, NM_181690.2 or NM_001206729.1; gene BRAFmay be identified as GenBank® Accession number NM_001354609.1 orNM_004333.5; gene FNTA may be identified as GenBank® Accession numberNM_002027.2; gene FNTB may be identified as GenBank® Accession numberNM_002028.3; gene MAP2K1 may be identified as GenBank® Accession numberNM_002755.3; gene MKNK1 may be identified as GenBank® Accession numberNM_003684.6, NM_198973.4 or NM_001135553.3; gene MKNK2 may be identifiedas GenBank® Accession number NM_017572.3 or NM_199054.2.

MF Profiles

A “molecular functional tumor portrait (MF profile),” as describedherein, refers to a graphical depiction of a tumor with regard tomolecular and cellular composition, and biological processes that arepresent within and/or surrounding the tumor. Related compositions andprocesses present within and/or surrounding a tumor are presented infunctional modules (also described herein as “gene groups”) of a MFprofile.

MF profiles may be constructed, in some embodiments, from geneexpression data (for example sequencing data, e.g., whole exomesequencing data, RNA sequencing data, or other gene expression data) ofnormal tissue and/or tumor tissue. FIG. 1A shows an exemplarybioinformatics pipeline for constructing a tumor portrait fromsequencing data. MF profiles produced in accordance with thebioinformatics pipeline in FIG. 1A may comprise functional modulesdepicted as circles and arrange in an circular pattern as shown in FIG.1B. Each circle of the MF profile in FIG. 1B represents a functionalmodule, which are labeled using lines. Related functional modules may becombined into a single functional module. For example, FIG. 1B showsthat the anti-metastatic factors module, the metastatic factors module,and the tumor suppressors module may be combined into the malignant cellproperties module.

FIG. 1C shows one embodiment of an MF profile as provided herein. Asshown in FIG. 1C, the MF profile 100 comprises 28 functional modules,three of which are labeled as 110, 120 and 130. Module size indicatesmodule intensity. For example, module 110 is larger than module 120indicating that module 110 has increased module intensity as compared tomodule 120. The presence or absence of cross-hatching of the moduleindicates whether the module is a pro-tumor module or an anti-tumormodule. Pro-tumor modules (e.g., module 120) are shown in solid shadeswithout cross-marking thereof, and anti-tumor modules (e.g., module 130)are shown with cross-marking thereof. The depth of shading of the moduleindicates module intensity. Modules relating to tumor malignancy 140 aredepicted in the top right quarter of the circle.

MF Profile Modules

A “functional module” or “gene group,” as described herein, refers torelated compositions and processes present within and/or surrounding atumor.

For example, an immune response/inflammation module provides informationrelated to immune system composition and activity within a tumor.Examples of immune system composition and activity within a tumorpresented in the immune response/inflammation module include, but arenot limited to, the number of unique tumor antigens, MHC-restrictedantigen presentation, expression of co-stimulatory compounds that areinvolved in T cell activation, intensities of activation and effectorphases of adaptive and innate immune responses, proportions of differentlymphoid and myeloid cell populations within a tumor, expression ratesof cancer-promoting and anti-cancer cytokines, and intensities of immuneresponse processes (e.g., activities of immunosuppressive cells andexpression of immune checkpoint inhibitory molecules).

Exemplary modules in a MF profile may include, but are not limited to,Major histocompatibility complex I (MHCI) module, Majorhistocompatibility complex II (MHCII) module, Coactivation moleculesmodule, Effector cells module, Effector T cell module; Natural killercells (NK cells) module, T cell traffic module, T cells module, B cellsmodule, B cell traffic module, Benign B cells module, Malignant B cellmarker module, M1 signatures module, Th1 signature module, Antitumorcytokines module, Checkpoint inhibition (or checkpoint molecules)module, Follicular dendritic cells module, Follicular B helper T cellsmodule, Protumor cytokines module, Regulatory T cells (Treg) module,Treg traffic module, Myeloid-derived suppressor cells (MDSCs) module,MDSC and TAM traffic module, Granulocytes module, Granulocytes trafficmodule, Eosinophil signature model, Neutrophil signature model, Mastcell signature module, M2 signature module, Th2 signature module, Th17signature module, Protumor cytokines module, Complement inhibitionmodule, Fibroblastic reticular cells module, Cancer associatedfibroblasts (CAFs) module, Matrix formation (or Matrix) module,Angiogenesis module, Endothelium module, Hypoxia factors module,Coagulation module, Blood endothelium module, Lymphatic endotheliummodule, Proliferation rate (or Tumor proliferation rate) module,Oncogenes module, PI3K/AKT/mTOR signaling module, RAS/RAF/MEK signalingmodule, Receptor tyrosine kinases expression module, Growth Factorsmodule, Tumor suppressors module, Metastasis signature module,Antimetastatic factors module, and Mutation status module. In certainembodiments, the modules may be described as “gene groups”.

In some embodiments, the gene groups of the modules may comprise atleast two genes (e.g., at least two genes, at least three genes, atleast four genes, at least five genes, at least six genes, at leastseven genes, at least eight genes, at least nine genes, at least tengenes, or more than ten genes as shown in the following lists; in someembodiments all of the listed genes are selected from each group; and insome embodiments the numbers of genes in each selected group are not thesame.

In some embodiments, the modules in a MF profile may comprise or consistof: Major histocompatibility complex I (MHCI) module, Majorhistocompatibility complex II (MHCII) module, Coactivation moleculesmodule, Effector cells (or Effector T cell) module, Natural killer cells(NK cells) module, T cells module, B cells module, M1 signatures module,Th1 signature module, Antitumor cytokines module, Checkpoint inhibition(or checkpoint molecules) module, Regulatory T cells (Treg) module,Myeloid-derived suppressor cells (MDSCs) module, Neutrophil signaturemodel, M2 signature module, Th2 signature module, Protumor cytokinesmodule, Complement inhibition module, Cancer associated fibroblasts(CAFs) module, Angiogenesis module, Endothelium module, Proliferationrate (or Tumor proliferation rate) module, PI3K/AKT/mTOR signalingmodule, RAS/RAF/MEK signaling module, Receptor tyrosine kinasesexpression module, Growth Factors module, Tumor suppressors module,Metastasis signature module, and Antimetastatic factors module. The MFprofile may additionally include: T cell traffic module, Antitumorcytokines module, Treg traffic module, MDSC and TAM traffic module,Granulocytes or Granulocyte traffic module, Eosinophil signature model,Mast cell signature module, Th17 signature module, Matrix formation (orMatrix) module, and Hypoxia factors module. Such an MF profile could beuseful for a subject with a solid cancer (e.g., a melanoma).

In some embodiments, the modules in a MF profile may comprise or consistof: Effector cells (or Effector T cell) module, Natural killer cells (NKcells) module, T cells module, Malignant B cell marker module, M1signatures module, Th1 signature module, Checkpoint inhibition (orcheckpoint molecules) module, Follicular dendritic cells module,Follicular B helper T cells module, Protumor cytokines module,Regulatory T cells (Treg) module, Neutrophil signature model, M2signature module, Th2 signature module, Complement inhibition module,Fibroblastic reticular cells module, Angiogenesis module, Bloodendothelium module, Proliferation rate (or Tumor proliferation rate)module, Oncogenes module, and Tumor suppressors module. The MF profilemay additionally include: Major histocompatibility complex I (MHCI)module, Major histocompatibility complex II (MHCII) module, Coactivationmolecules module, B cell traffic module, Benign B cells module,Antitumor cytokines module, Treg traffic module, Mast cell signaturemodule, Th17 signature module, Matrix formation (or Matrix) module,Hypoxia factors module, Coagulation module, and Lymphatic endotheliummodule. Such an MF profile could be useful for a subject with follicularlymphoma. In some embodiments, the gene groups of the modules maycomprise at least two genes (e.g., at least two genes, at least threegenes, at least four genes, at least five genes, at least six genes, atleast seven genes, at least eight genes, at least nine genes, at leastten genes, or more than ten genes as shown in the following lists; insome embodiments all of the listed genes are selected from each group;and in some embodiments the numbers of genes in each selected group arenot the same): Major histocompatibility complex I (MHCI) module: HLA-A,HLA-B, HLA-C, B2M, TAP1, and TAP2; Major histocompatibility complex II(MHCII) module: HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA,HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2,HLA-DQB2, and HLA-DRB6; Coactivation molecules module: CD80, CD86, CD40,CD83, TNFRSF4, ICOSLG, CD28; Effector cells module: IFNG, GZMA, GZMB,PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, and CD8B;Effector T cell module: IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY,FASLG, TBX21, EOMES, CD8A, and CD8B; Natural killer cells (NK cells)module: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG,KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5, EOMES, CLIC3,FGFBP2, KLRF1, and SH2D1B; T cell traffic module: CXCL9, CXCL10, CXCR3,CX3CL1, CCR7, CXCL11, CCL21, CCL2, CCL3, CCL4, and CCL5; T cells module:EOMES, TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A,TRAT1, CD5, and CD28; B cells module: CD19, MS4A1, TNFRSF13C, CD27,CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5,and STAP1; B cell traffic module: CXCL13 and CXCR5; Benign B cellsmodule: CD19, MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B,CD22, CD79A, CD79B, and BLK; Malignant B cell marker module: MME, CD70,CD20, CD22, and PAX5; M1 signatures module: NOS2, IL12A, IL12B, IL23A,TNF, IL1B, and SOCS3; Th1 signature module: IFNG, IL2, CD40LG, IL15,CD27, TBX21, LTA, and IL21; Antitumor cytokines module: HMGB1, TNF,IFNB1, IFNA2, CCL3, TNFSF10, and FASLG; Checkpoint inhibition (orcheckpoint molecules) module: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA,HAVCR2, and VSIR; Follicular dendritic cells module: CR1, FCGR2A,FCGR2B, FCGR2C, CR2, FCER2, CXCL13, MADCAM1, ICAM1, VCAM1, BST1, LTBR,and TNFRSF1A; Follicular B helper T cells module: CXCR5, B3GAT1, ICOS,CD40LG, CD84, IL21, BCL6, MAF, and SAP; Protumor cytokines module: IL10,TGFB1, TGFB2, TGFB3, IL22, MIF, TNFSF13B, IL6, and IL7; Regulatory Tcells (Treg) module: TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF18,TNFR2, and TNFRSF1B; Treg traffic module: CCL17, CXCL12, CXCR4, CCR4,CCL22, CCL1, CCL2, CCL5, CXCL13, and CCL28; Myeloid-derived suppressorcells (MDSCs) module: IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2,CYBB, CXCR4, and CD33; MDSC and TAM traffic module: CXCL1, CXCL5, CCL2,CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, and CXCL8; Granulocytes module:CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, CCL26,PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, MPO, ELANE,PRTN3, CTSG, FCGR3B, CXCR1, CXCR2, CD177, PI3, FFAR2, PGLYRP1, CMA1,TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, and SIGLEC8; Granulocyte trafficmodule: CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, andCCL26; Eosinophil signature model: PRG2, EPX, RNASE2, RNASE3, IL5RA,GATA1, SIGLEC8, and PRG3; Neutrophil signature model: MPO, ELANE, PRTN3,CTSG, FCGR3B, CXCR1, CXCR2, CD177, PI3, FFAR2, and PGLYRP1; Mast cellsignature module: CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, andSIGLEC8; M2 signature module: IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1,CSF1, LRP1, ARG1, PTGS1, MSR1, CD163, and CSF1R; Th2 signature module:IL4, IL5, IL13, IL10, IL25, and GATA3; Th17 signature module: IL17A,IL22, IL26, IL17F, IL21, and RORC; Protumor cytokines module: IL10,TGFB1, TGFB2, TGFB3, IL22, and MIF; Complement inhibition module: CFD,CFI, CD55, CD46, CR1, and CD59; Fibroblastic reticular cells module:DES, VIM, PDGFRA, PDPN, NT5E, THY1, ENG, ACTA2, LTBR, TNFRSF1A, VCAM1,ICAM1, and BST1; Cancer associated fibroblasts (CAFs) module: COL1A1,COL1A2, COL4A1, COL5A1, TGFB1, TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1,CD248, COL6A1, COL6A2, COL6A3, FBLN1, LUM, MFAP5, LGALS1, and PRELP;Matrix formation (or Matrix) module: MMP9, FN1, COL1A1, COL1A2, COL3A1,COL4A1, CA9, VTN, LGALS7, TIMP1, MMP2, MMP1, MMP3, MMP12, LGALS9, MMP7,and COL5A1; Angiogenesis module: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8,CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3,VCAM1, MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3;Endothelium module: VEGFA, NOS3, KDR, FLT1, VCAM1, VWF, CDH5, MMRN1,CLEC14A, MMRN2, and ECSCR; Hypoxia factors module: LDHA, HIF1A, EPAS1,CA9, SPP1, LOX, SLC2A1, and LAMP3; Coagulation module: HPSE, SERPINE1,SERPINB2, F3, and ANXA2; Blood endothelium module: VEGFA, NOS3, KDR,FLT1, VCAM1, VWF, CDH5, and MMRN1; Lymphatic endothelium module: CCL21and CXCL12; Proliferation rate (or Tumor proliferation rate) module:MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2,BUB1, PLK1, PRC1, CCNB1, MCM2, MCM6, CDK4, and CDK6; Oncogenes module:MDM2, MYC, AKT1, BCL2, MME, and SYK; PI3K/AKT/mTOR signaling module:PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, and AKT3;RAS/RAF/MEK signaling module: BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1,and MKNK2; Receptor tyrosine kinases expression module: ALK, AXL, KIT,EGFR, ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3,BCR-ABL, PDGFRA, PDGFRB, and ABL1; Growth Factors module: NGF, CSF3,CSF2, FGF7, IGF1, IGF2, IL7, and FGF2; Tumor suppressors module: TP53,MLL2, CREBBP, EP300, ARID1A, HIST1H1, EBF1, IRF4, IKZF3, KLHL6, PRDM1,CDKN2A, RB1, EPHA7, TNFAIP3, TNFRSF14, FAS, SHP1, SOCS1, SIK1, PTEN,DCN, MTAP, AIM2, and MITF; Metastasis signature module: ESRP1, HOXA1,SMARCA4, TWIST1, NEDD9, PAPPA, CTSL, SNAI2, and HPSE; Antimetastaticfactors module: NCAM1, CDH1, KISS1, BRMS1, ADGRG1, TCF21, PCDH10, andMITF; and Mutation status module: APC, ARID1A, ATM, ATRX, BAP1, BRAF,BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3,HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS,PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, andVHL. In certain embodiments, two or more genes from any combination ofthe listed modules may be included in an MF portrait.

In some embodiments, the gene groups of the modules may comprise atleast two genes (e.g., at least two genes, at least three genes, atleast four genes, at least five genes, at least six genes, at leastseven genes, at least eight genes, at least nine genes, at least tengenes, or more than ten genes as shown in the following lists; in someembodiments all of the listed genes are selected from each group; and insome embodiments the numbers of genes in each selected group are not thesame): Major histocompatibility complex I (MHCI) module: HLA-A, HLA-B,HLA-C, B2M, TAP1, and TAP2; Major histocompatibility complex II (MHCII)module; HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA,HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2,HLA-DQB2, and HLA-DRB6; Coactivation molecules module: CD80, CD86, CD40,CD83, TNFRSF4, ICOSLG, CD28; Effector cells (or Effector T cell) module:IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES,CD8A, and CD8B; Natural killer cells (NK cells) module: NKG7, CD160,CD244, NCR1, KLRC2, KLRK1, CD226, GNLY, KIR2DL4, KIR2DS1, KIR2DS2,KIR2DS3, KIR2DS4, KIR2DS5, EOMES, CLIC3, FGFBP2, KLRF1, and SH2D1B; Tcells module: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK,UBASH3A, TRAT1, CD5, and CD28; B cells module: CD19, MS4A1, TNFRSF13C,CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5,PAX5, and STAP1; M1 signatures module: NOS2, IL12A, IL12B, IL23A, TNF,IL1B, and SOCS3; Th1 signature module: IFNG, IL2, CD40LG, IL15, CD27,TBX21, LTA, and IL21; Checkpoint inhibition (or checkpoint molecules)module: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, and VSIR;Regulatory T cells (Treg) module: TGFB1, TGFB2, TGFB3, FOXP3, CTLA4,IL10, and TNFRSF1B; Myeloid-derived suppressor cells (MDSCs) module:IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB, CXCR4, andCD33; Neutrophil signature model: MPO, ELANE, PRTN3, CTSG, FCGR3B,CXCR1, CXCR2, CD177, PI3, FFAR2, and PGLYRP1; M2 signature module: IL10,VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1, CD163,and CSF1R; Th2 signature module: IL4, IL5, IL13, IL10, IL25, and GATA3;Protumor cytokines module: IL10, TGFB1, TGFB2, TGFB3, IL22, and MIF;Complement inhibition module: CFD, CFI, CD55, CD46, and CR1; Cancerassociated fibroblasts (CAFs) module: COL1A1, COL1A2, COL4A1, COL5A1,TGFB1, TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2,COL6A3, FBLN1, LUM, MFAP5, and PRELP; Angiogenesis module: VEGFA, VEGFB,VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK,VWF, CDH5, NOS3, VCAM1, and MMRN1; Endothelium module: VEGFA, NOS3, KDR,FLT1, VCAM1, VWF, CDH5, MMRN1, CLEC14A, MMRN2, and ECSCR; Proliferationrate (or Tumor proliferation rate) module: MKI67, ESCO2, CETN3, CDK2,CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, MCM6,CDK4, and CDK6; PI3K/AKT/mTOR signaling module: PIK3CA, PIK3CB, PIK3CG,PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, and AKT3; RAS/RAF/MEK signalingmodule: BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, and MKNK2; Receptortyrosine kinases expression module: ALK, AXL, KIT, EGFR, ERBB2, FLT3,MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA, PDGFRB,and ABL1; Growth Factors module: NGF, CSF3, CSF2, FGF7, IGF1, IGF2, IL7,and FGF2; Tumor suppressors module: TP53, SIK1, PTEN, DCN, MTAP, AIM2,RB1, and MITF; Metastasis signature module: ESRP1, HOXA1, SMARCA4,TWIST1, NEDD9, PAPPA, and HPSE; and Antimetastatic factors module:NCAM1, CDH1, KISS1, and BRMS1. In some embodiments, the gene groups ofthe modules may further comprise at least two genes (e.g., at least twogenes, at least three genes, at least four genes, at least five genes,at least six genes, at least seven genes, at least eight genes, at leastnine genes, at least ten genes, or more than ten genes as shown in thefollowing lists; in some embodiments all of the listed genes areselected from each group; and in some embodiments the numbers of genesin each selected group are not the same): T cell traffic module: CXCL9,CXCL10, CXCR3, CX3CL1, CCR7, CXCL11, CCL21, CCL2, CCL3, CCL4, and CCL5;Antitumor cytokines module: HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, andFASLG; Treg traffic module: CCL17, CXCL12, CXCR4, CCR4, CCL22, CCL1,CCL2, CCL5, CXCL13, and CCL28; MDSC and TAM traffic module: CXCL1,CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, and CXCL8; Granulocytetraffic module: CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5,CCR3, and CCL26; Eosinophil signature model: PRG2, EPX, RNASE2, RNASE3,IL5RA, GATA1, SIGLEC8, and PRG3; Mast cell signature module: CMA1,TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, and SIGLEC8; Th17 signature module:IL17A, IL22, IL26, IL17F, IL21, and RORC; Matrix formation (or Matrix)module: FN1, CA9, MMP1, MMP3, MMP12, LGALS9, MMP7, MMP9, COL1A1, COL1A2,COL4A1, and COL5A1; and Hypoxia factors module: LDHA, HIF1A, EPAS1, CA9,SPP1, LOX, SLC2A1, and LAMP3. In certain embodiments, two or more genesfrom each of the listed modules are included. Any of the foregoing setsof modules may be used in a MF portrait for a subject with a solidcancer (e.g., melanoma).

In some embodiments, the gene groups of the modules may comprise atleast two genes (e.g., at least two genes, at least three genes, atleast four genes, at least five genes, at least six genes, at leastseven genes, at least eight genes, at least nine genes, at least tengenes, or more than ten genes as shown in the following lists; in someembodiments all of the listed genes are selected from each group; and insome embodiments the numbers of genes in each selected group are not thesame): Effector T cell module: IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70,GNLY, FASLG, TBX21, EOMES, CD8A, and CD8B; Natural killer cells (NKcells) module: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH,GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, and KIR2DS5; Tcells module: EOMES, TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2,LCK, UBASH3A, and TRAT1; Benign B cells module: CD19, MS4A1, TNFRSF13C,CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, and BLK;Malignant B cell marker module: MME, CD70, CD20, CD22, and PAX5; M1signatures module: NOS2, IL12A, IL12B, IL23A, TNF, IL1B, and SOCS3; Th1signature module: IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA, and IL21;Checkpoint inhibition (or checkpoint molecules) module: PDCD1, CD274,CTLA4, LAG3, PDCD1LG2, BTLA, and HAVCR2; Follicular dendritic cellsmodule: CR1, FCGR2A, FCGR2B, FCGR2C, CR2, FCER2, CXCL13, MADCAM1, ICAM1,VCAM1, BST1, LTBR, and TNFRSF1A; Follicular B helper T cells module:CXCR5, B3GAT1, ICOS, CD40LG, CD84, IL21, BCL6, MAF, and SAP; Protumorcytokines module: IL10, TGFB1, TGFB2, TGFB3, IL22, MIF, TNFSF13B, IL6,and IL7; Regulatory T cells (Treg) module: TGFB1, TGFB2, TGFB3, FOXP3,CTLA4, IL10, TNFRSF18, and TNFR2; Neutrophil signature model: MPO,ELANE, PRTN3, and CTSG; M2 signature module: IL10, VEGFA, TGFB1, IDO1,PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1, CD163, and CSF1R; Th2signature module: IL4, IL5, IL13, IL10, IL25, and GATA3; Complementinhibition module: CFD, CFI, CD55, CD46, CR1, and CD59; Fibroblasticreticular cells module: DES, VIM, PDGFRA, PDPN, NT5E, THY1, ENG, ACTA2,LTBR, TNFRSF1A, VCAM1, ICAM1, and BST1; Angiogenesis module: VEGFA,VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1,ANGPT2, TEK, VWF, and CDH5; Blood endothelium module: VEGFA, NOS3, KDR,FLT1, VCAM1, VWF, CDH5, and MMRN1; Proliferation rate (or Tumorproliferation rate) module: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1,AURKA, AURKB, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, and MCM6; Oncogenesmodule: MDM2, MYC, AKT1, BCL2, MME, and SYK; and Tumor suppressorsmodule: TP53, MLL2, CREBBP, EP300, ARID1A, HIST1H1, EBF1, IRF4, IKZF3,KLHL6, PRDM1, CDKN2A, RB1, EPHA7, TNFAIP3, TNFRSF14, FAS, SHP1, andSOCS1. In some embodiments, the gene groups of the modules may furthercomprise at least two genes (e.g., at least two genes, at least threegenes, at least four genes, at least five genes, at least six genes, atleast seven genes, at least eight genes, at least nine genes, at leastten genes, or more than ten genes as shown in the following lists; insome embodiments all of the listed genes are selected from each group;and in some embodiments the numbers of genes in each selected group arenot the same): Coactivation molecules module: TNFRSF4 and CD28; B celltraffic module: CXCL13 and CXCR5; Antitumor cytokines module: HMGB1,TNF, IFNB1, IFNA2, CCL3, TNFSF10, FASLG; Treg traffic module: CCL17,CCR4, CCL22, and CXCL13; Eosinophil signature model: PRG2, EPX, RNASE2,RNASE3, IL5RA, GATA1, SIGLEC8, and PRG3; Mast cell signature module:CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, and SIGLEC8; Th17 signaturemodule: IL17A, IL22, IL26, IL17F, IL21, and RORC; Matrix formation (orMatrix) module: MMP9, FN1, COL1A1, COL1A2, COL3A1, COL4A1, CA9, VTN,LGALS7, TIMP1, and MMP2; Hypoxia factors module: LDHA, HIF1A, EPAS1,CA9, SPP1, LOX, SLC2A1, and LAMP3; Coagulation module: HPSE, SERPINE1,SERPINB2, F3, and ANXA2; and Lymphatic endothelium module: CCL21 andCXCL12. In certain embodiments, two or more genes from each of thelisted modules are included. Any of the foregoing sets of modules may beused in a MF portrait for a subject with a follicular lymphoma.

In some embodiments, the plurality of gene groups (or modules)associated with cancer malignancy is the tumor properties group. In someembodiments, the plurality of gene groups associated with cancermicroenvironment are the tumor-promoting immune microenvironment group,the anti-tumor immune microenvironment group, the angiogenesis group,and the fibroblasts group.

In certain embodiments, the plurality of gene groups associated withcancer malignancy comprises at least three genes from the followinggroup (e.g., at least three genes, at least four genes, at least fivegenes, at least six genes, at least seven genes, at least eight genes,at least nine genes, at least ten genes, or more than ten genes areselected from each group; in some embodiments all of the listed genesare selected from each group): the tumor properties group: MKI67, ESCO2,CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, CDK4, CDK6, PRC1, E2F1, MYBL2,BUB1, PLK1, CCNB1, MCM2, MCM6, PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1,MTOR, PTEN, PRKCA, AKT2, AKT3, BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1,MKNK2, ALK, AXL, KIT, EGFR, ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2,FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA, PDGFRB, NGF, CSF3, CSF2, FGF7,IGF1, IGF2, IL7, FGF2, TP53, SIK1, PTEN, DCN, MTAP, AIM2, RB1, ESRP1,CTSL, HOXA1, SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA, HPSE, KISS1, ADGRG1,BRMS1, TCF21, CDH1, PCDH10, NCAM1, MITF, APC, ARID1A, ATM, ATRX, BAP1,BRAF, BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3,GATA3, HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1,NRAS, PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53,and VHL. In certain embodiments, the plurality of gene groups associatedwith cancer microenvironment includes at least three genes from each ofthe following groups (e.g., at least three genes, at least four genes,at least five genes, at least six genes, at least seven genes, at leasteight genes, at least nine genes, at least ten genes, or more than tengenes are selected from each group; in some embodiments all of thelisted genes are selected from each group): the anti-tumor immunemicroenvironment group: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, HLA-DRA,HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1,HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, HLA-DRB6,CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, CD28, IFNG, GZMA, GZMB, PRF1,LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B, NKG7, CD160,CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1,KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5, CXCL9, CXCL10, CXCR3, CX3CL1, CCR7,CXCL11, CCL21, CCL2, CCL3, CCL4, CCL5, EOMES, TBX21, ITK, CD3D, CD3E,CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, TRAT1, CD19, MS4A1, TNFRSF13C,CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, NOS2,IL12A, IL12B, IL23A, TNF, IL1B, SOCS3, IFNG, IL2, CD40LG, IL15, CD27,TBX21, LTA, IL21, HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG;the tumor-promoting immune microenvironment group: PDCD1, CD274, CTLA4,LAG3, PDCD1LG2, BTLA, HAVCR2, VSIR, CXCL12, TGFB1, TGFB2, TGFB3, FOXP3,CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5,CXCL13, CCL28, IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB,CXCR4, CD33, CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1,CXCL8, CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3,CCL26, PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1,TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, CTSG,IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1,CD163, CSF1R, IL4, IL5, IL13, IL10, IL25, GATA3, IL10, TGFB1, TGFB2,TGFB3, IL22, MIF, CFD, CFI, CD55, CD46, and CR1; the fibroblasts group:LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2, TGFB3, ACTA2,FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, and COL6A3; and the angiogenesisgroup: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR,ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1, MMRN1, LDHA, HIF1A,EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMPS. In some embodiments, anunequal number of genes may be selected from each of the listed groupsfor use. In specific embodiments, all or almost all of the listed genesare used.

In some embodiments, the plurality of gene groups associated with cancermalignancy are: the proliferation rate group, the PI3K/AKT/mTORsignaling group, the RAS/RAF/MEK signaling group, the receptor tyrosinekinases expression group, the tumor suppressors group, the metastasissignature group, the anti-metastatic factors group, and the mutationstatus group. In some embodiments, the plurality of gene groupsassociated with cancer microenvironment are: the cancer associatedfibroblasts group, the angiogenesis group, the antigen presentationgroup, the cytotoxic T and NK cells group, the B cells group, theanti-tumor microenvironment group, the checkpoint inhibition group, theTreg group, the MDSC group, the granulocytes group, and thetumor-promotive immune group.

In some embodiments, the plurality of gene groups associated with cancermalignancy comprises at least three genes from each of the followinggroups (e.g., at least three genes, at least four genes, at least fivegenes, at least six genes, at least seven genes, at least eight genes,at least nine genes, at least ten genes, or more than ten genes areselected from each group): the proliferation rate group: MKI67, ESCO2,CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, CDK4, CDK6, PRC1, E2F1, MYBL2,BUB1, PLK1, CCNB1, MCM2, and MCM6; the PI3K/AKT/mTOR signaling group:PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, and AKT3;the RAS/RAF/MEK signaling group: BRAF, FNTA, FNTB, MAP2K1, MAP2K2,MKNK1, and MKNK2; the receptor tyrosine kinases expression group: ALK,AXL, KIT, EGFR, ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4,ERBB3, BCR-ABL, PDGFRA, and PDGFRB; the tumor suppressors group: TP53,SIK1, PTEN, DCN, MTAP, AIM2, and RB1; the metastasis signature group:ESRP1, CTSL, HOXA1, SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA, and HPSE; theanti-metastatic factors group: KISS1, ADGRG1, BRMS1, TCF21, CDH1,PCDH10, NCAM1, and MITF; and the mutation status group: APC, ARID1A,ATM, ATRX, BAP1, BRAF, BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR,FBXW7, FLT3, GATA3, HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1,NOTCH1, NPM1, NRAS, PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2,STAG2, TAF1, TP53, and VHL.

In some embodiments, the plurality of gene groups associated with cancermicroenvironment comprises at least three genes from each of thefollowing groups (e.g., at least three genes, at least four genes, atleast five genes, at least six genes, at least seven genes, at leasteight genes, at least nine genes, at least ten genes, or more than tengenes are selected from each group): the cancer associated fibroblastsgroup: LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2, TGFB3,ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, and COL6A3; theangiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1,PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1,MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3; theantigen presentation group: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2,HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1,HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2,HLA-DRB6, CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, and CD28; thecytotoxic T and NK cells group: IFNG, GZMA, GZMB, PRF1, LCK, GZMK,ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B, NKG7, CD160, CD244, NCR1,KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2,KIR2DS3, KIR2DS4, KIR2DS5, CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11,CCL21, CCL2, CCL3, CCL4, CCL5, EOMES, TBX21, ITK, CD3D, CD3E, CD3G,TRAC, TRBC1, TRBC2, LCK, UBASH3A, and TRAT1; the B cells group: CD19,MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A,CD79B, and BLK; the anti-tumor microenvironment group: NOS2, IL12A,IL12B, IL23A, TNF, IL1B, SOCS3, IFNG, IL2, CD40LG, IL15, CD27, TBX21,LTA, IL21, HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG; thecheckpoint inhibition group: PDCD1, CD274, CTLA4, LAGS, PDCD1LG2, BTLA,HAVCR2, and VSIR; the Treg group: CXCL12, TGFB1, TGFB2, TGFB3, FOXP3,CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5,CXCL13, and CCL28; the MDSC group: IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2,TGFB3, NOS2, CYBB, CXCR4, CD33, CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2,CCL3, CCL5, CSF1, and CXCL8; the granulocytes group: CXCL8, CXCL2,CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, CCL26, PRG2, EPX, RNASE2,RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1, TPSAB1, MS4A2, CPA3, IL4,IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, and CTSG; the tumor-promotiveimmune group: IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1,PTGS1, MSR1, CD163, CSF1R, IL4, IL5, IL13, IL10, IL25, GATA3, IL10,TGFB1, TGFB2, TGFB3, IL22, MIF, CFD, CFI, CD55, CD46, and CR1. In someembodiments, an unequal number of genes may be selected from each of thelisted groups for use. In specific embodiments, all or almost all of thelisted genes are used.

In some embodiments, the plurality of gene groups associated with cancermalignancy are: the proliferation rate group, the PI3K/AKT/mTORsignaling group, the RAS/RAF/MEK signaling group, the receptor tyrosinekinases expression group, the growth factors group, the tumorsuppressors group, the metastasis signature group, the anti-metastaticfactors group, and the mutation status group. In some embodiments, theplurality of gene groups associated with cancer microenvironment are:the cancer associated fibroblasts group, the angiogenesis group, theMHCI group, the MHCII group, the coactivation molecules group, theeffector cells group, the NK cells group, the T cell traffic group, theT cells group, the B cells group, the M1 signatures group, the Th1signature group, the antitumor cytokines group, the checkpointinhibition group, the Treg group, the MDSC group, the granulocytesgroup, the M2 signature group, the Th2 signature group, the protumorcytokines group, and the complement inhibition group.

In some embodiments, the plurality of gene groups associated with cancermalignancy comprises at least three genes from each of the followinggroups (e.g., at least three genes, at least four genes, at least fivegenes, at least six genes, at least seven genes, at least eight genes,at least nine genes, at least ten genes, or more than ten genes areselected from each group): the proliferation rate group: MKI67, ESCO2,CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, CDK4, CDK6, PRC1, E2F1, MYBL2,BUB1, PLK1, CCNB1, MCM2, and MCM6; the PI3K/AKT/mTOR signaling group:PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, and AKT3;the RAS/RAF/MEK signaling group: BRAF, FNTA, FNTB, MAP2K1, MAP2K2,MKNK1, and MKNK2; the receptor tyrosine kinases expression group: ALK,AXL, KIT, EGFR, ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4,ERBB3, BCR-ABL, PDGFRA, and PDGFRB; the growth factors group: NGF, CSF3,CSF2, FGF7, IGF1, IGF2, IL7, and FGF2; the tumor suppressors group:TP53, SIK1, PTEN, DCN, MTAP, AIM2, and RB1; the metastasis signaturegroup: ESRP1, CTSL, HOXA1, SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA, andHPSE; the anti-metastatic factors group: KISS1, ADGRG1, BRMS1, TCF21,CDH1, PCDH10, NCAM1, and MITF; and the mutation status group: APC,ARID1A, ATM, ATRX, BAP1, BRAF, BRCA2, CDH1, CDKN2A, CTCF, CTNNB1,DNMT3A, EGFR, FBXW7, FLT3, GATA3, HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3,NCOR1, NF1, NOTCH1, NPM1, NRAS, PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1,SETD2, STAG2, TAF1, TP53, and VHL. In some embodiments, the plurality ofgene groups associated with cancer microenvironment comprises at leastthree genes from each of the following groups: the cancer associatedfibroblasts group: LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2,TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, and COL6A3; theangiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1,PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1,MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3; the MHCIgroup: HLA-A, HLA-B, HLA-C, B2M, TAP1, and TAP2; the MHCII group:HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1,HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, andHLA-DRB6; the coactivation molecules group: CD80, CD86, CD40, CD83,TNFRSF4, ICOSLG, and CD28; the effector cells group: IFNG, GZMA, GZMB,PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, and CD8B; theNK cells group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH,GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, and KIR2DS5;the T cell traffic group: CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11,CCL21, CCL2, CCL3, CCL4, and CCL5; the T cells group: EOMES, TBX21, ITK,CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, and TRAT1; the Bcells group: CD19, MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17,TNFRSF13B, CD22, CD79A, CD79B, and BLK; the M1 signatures group: NOS2,IL12A, IL12B, IL23A, TNF, IL1B, and SOCS3; the Th1 signature group:IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA, and IL21; the antitumorcytokines group: HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG; thecheckpoint inhibition group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA,HAVCR2, and VSIR; the Treg group: CXCL12, TGFB1, TGFB2, TGFB3, FOXP3,CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5,CXCL13, and CCL28; the MDSC group: IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2,TGFB3, NOS2, CYBB, CXCR4, CD33, CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2,CCL3, CCL5, CSF1, and CXCL8; the granulocytes group: CXCL8, CXCL2,CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, CCL26, PRG2, EPX, RNASE2,RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1, TPSAB1, MS4A2, CPA3, IL4,IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, and CTSG; the M2 signature group:IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1,CD163, and CSF1R; the Th2 signature group: IL4, IL5, IL13, IL10, IL25,and GATA3; the protumor cytokines group: IL10, TGFB1, TGFB2, TGFB3,IL22, and MIF; and the complement inhibition group: CFD, CFI, CD55,CD46, and CR1. In some embodiments, an unequal number of genes may beselected from each of the listed groups for use. In specificembodiments, all or almost all of the listed genes are used.

MF profiles may depict the intensity (e.g., amount) of a module or genegroup using a distinguishing feature (e.g., color, shading or pattern,size, and/or shape). As used herein, “intensity” refers to an amount ofa gene group expression level within a MF profile. For example, 2^(nd)MF profile type cancers have an intense proliferation rate moduleindicative of a high proliferation rate of such cancers. Accordingly, in2^(nd) MF profile type cancers, the proliferation rate module isdepicted in a larger size as an indication that this module is moreabundant in the tumor than other modules. In some embodiments, the MFprofile comprises modules of various sizes in which module size isindicative of module intensity. In some embodiments, the MF profilecomprises modules of increasing sizes in which increasing module size isindicative of increasing module intensity.

MF profiles may depict a module as a pro-tumor module or anti-tumormodule using a distinguishing feature (e.g., color, shading or pattern,size, and/or shape). In some embodiments, the MF profile comprises apro-tumor module as one color or pattern and an anti-tumor module asanother color or pattern. In some embodiments, the MF profile comprisesa pro-tumor module as burgundy or a shade thereof and an anti-tumormodule as blue or a shade thereof. In some embodiments, the MF profilecomprises a pro-tumor module as solid shades without cross-marking andan anti-tumor module as shades with cross-marking.

MF profiles may comprise any number of functional modules. In someembodiments, the MF profile comprises at least 2, at least 3, at least4, at least 5, at least 6, at least 7, at least 8, at least 9, at least10, at least 11, at least 12, at least 13, at least 14, at least 15, atleast 16, at least 17, at least 18, at least 19, at least 20, at least21, at least 22, at least 23, at least 24, at least 25, at least 26, atleast 27, or at least 28 modules. In some embodiments, the MF profilecomprises up to 2, up to 3, up to 4, up to 5, up to 6, up to 7, up to 8,up to 9, up to 10, up to 11, up to 12, up to 13, up to 14, up to 15, upto 16, up to 17, up to 18, up to 19, up to 20, up to 21, up to 22, up to23, up to 24, up to 25, up to 26, up to 27, or up to 28 modules.

MF Profile Types

The present disclosure is based, in part, on the finding that variouscancers (e.g., tumors) can be categorized into four types (i.e., firstMF profile type or “1^(st) MF profile,” second MF profile type or“2^(nd) MF profile,” third MF profile type or “3^(rd) MF profile,” andfourth MF profile type or “4^(th) MF profile” cancers) based on certainproperties of the cancer or tumor (e.g., expression data).

As used herein, the term “cancer type,” “tumor type,” or “MF profiletype” refers to a cancer (e.g., a tumor) having certain featuresincluding certain molecular and cellular compositions, and biologicalprocesses.

MF profile type, in some embodiments, may provide information relatingto a level of immune cells within and/or surrounding a tumor. Forexample, an “inflamed” or “hot” MF profile type includes a cancer (e.g.,a tumor) that is highly infiltrated by immune cells, a “non-inflamed” or“cold” MF profile type describes a cancer (e.g., a tumor) that is poorlyinfiltrated by immune cells. In some embodiments, describing a cancer asa 1^(st) MF profile type cancer indicates that the cancer (e.g., atumor) is inflamed. In some embodiments, describing a cancer as a 2^(nd)MF profile type cancer indicates that the cancer (e.g., a tumor) isinflamed. In some embodiments, describing a cancer as a 3^(rd) MFprofile type cancer indicates that the cancer (e.g., a tumor) isnon-inflamed. In some embodiments, describing a cancer as a 4^(th) MFprofile type cancer indicates that the cancer (e.g., a tumor) isnon-inflamed.

MF profile type, in some embodiments, provides information relating toan average ratio of malignant to nonmalignant cells of a tumor (e.g.,tumor purity). In some embodiments, the average ratio of malignant tononmalignant cells increases with MF profile type. For example, 4^(th)MF profile >3^(rd) MF profile >2^(nd) MF profile >1^(st) MF profile withrespect to an average ratio of malignant to nonmalignant cells.

In some embodiments, describing a cancer as a 1^(st) MF profile typecancer indicates that the tumor has about 2 times (twice) as manynonmalignant cells as malignant cells. In some embodiments, describing acancer as a 1^(st) MF profile type cancer indicates that the tumor hasan average ratio of malignant to nonmalignant cells of between 0.4 to0.6. In some embodiments, describing a cancer as a 1^(st) MF profiletype cancer indicates that the cancer has an average ratio of malignantto nonmalignant cells of about 0.5.

In some embodiments, describing a cancer as a 2^(nd) MF profile typecancer indicates that the cancer has about 1.5 times as manynonmalignant cells as malignant cells. In some embodiments, describing acancer as a 2^(nd) MF profile type cancer indicates that the cancer hasan average ratio of malignant to nonmalignant cells between 0.6 to 0.7.In some embodiments, describing a cancer as a 2^(nd) MF profile typecancer indicates that the cancer has an average ratio of malignant tononmalignant cells of about 0.65.

In some embodiments, describing a cancer as a 3^(rd) MF profile typecancer indicates that the cancer has about 1.3 times as manynonmalignant cells as malignant cells. In some embodiments, describing acancer as a 3^(rd) MF profile type cancer indicates that the cancer hasan average ratio of malignant to nonmalignant cells between 0.7 to 0.8.In some embodiments, describing a cancer as a 3^(rd) MF profile typecancer indicates that a tumor has an average ratio of malignant tononmalignant cells of about 0.8.

In some embodiments, describing a cancer as a 4^(th) MF profile typecancer indicates that the cancer has about 1.1 times as manynonmalignant cells s malignant cells. In some embodiments, describing acancer as a 4^(th) MF profile type cancer indicates that the cancer hasan average ratio of malignant to nonmalignant cells between 0.8 to 0.9.In some embodiments, describing a cancer as a 4^(th) MF profile typecancer indicates that the cancer has an average ratio of malignant tononmalignant cells of about 0.85.

MF profile type, in some embodiments, provides information relating totumor vascularization. In some embodiments, describing a cancer as a1^(st) MF profile type cancer indicates that the cancer (e.g., thetumor) is vascularized. In some embodiments, describing a cancer as a2^(nd) MF profile type cancer indicates that the cancer (e.g., thetumor) is non-vascularized. In some embodiments, describing a cancer asa 3^(rd) MF profile type cancer indicates that the cancer (e.g., thetumor) is vascularized. In some embodiments, describing a cancer as a4^(th) MF profile type cancer indicates that the cancer (e.g., thetumor) is non-vascularized.

MF profile type, in some embodiments, provides information relating tolevels of cancer associated fibroblasts (CAFs) within and/or surroundinga tumor. In some embodiments, describing a cancer as a 1^(st) MF profiletype cancer indicates that the cancer (e.g., the tumor) comprises CAFs.In some embodiments, describing a cancer as a 2^(nd) MF profile typecancer indicates that the cancer (e.g., the tumor) is devoid of CAFs. Insome embodiments, describing a cancer as a 3^(rd) MF profile type cancerindicates that the cancer (e.g., the tumor) comprises CAFs. In someembodiments, describing a cancer as a 4^(th) MF profile type cancerindicates that the cancer (e.g., the tumor) is devoid of CAFs.

MF profile type, in some embodiments, provides information relating totumor proliferation rates. In some embodiments, describing a cancer as a1^(st) MF profile type cancer indicates that the cancer (e.g., thetumor) has an average proliferation rate. In some embodiments,describing a cancer as a 2^(nd) MF profile type cancer indicates thatthe cancer (e.g., the tumor) has a high proliferation rate. In someembodiments, describing a cancer as a 3^(rd) MF profile type cancerindicates that the cancer (e.g., the tumor) has an average proliferationrate. In some embodiments, describing a cancer as a 4^(th) MF profiletype cancer indicates that the cancer (e.g., the tumor) has a highproliferation rate.

MF profile type, in some embodiments, provides information relating topatient survival rate. In some embodiments, the patient survival rateincreases with MF profile type. For example, 1^(st) MF profile >2^(nd)MF profile >3^(rd) MF profile >4^(th) MF profile with respect to patientsurvival rate.

In some embodiments, describing a cancer as a 1^(st) MF profile typecancer indicates a good patient survival rate. In some embodiments,describing a cancer as a 2^(nd) MF profile type cancer indicates anoptimal patient survival rate. In some embodiments, describing a canceras a 3^(rd) MF profile type cancer indicates that a poor patientsurvival rate. In some embodiments, describing a cancer as a 4^(th) MFprofile type cancer indicates that a poor patient survival rate.

MF profile type, in some embodiments, provides information relating topatient treatment. In some embodiments, the MF profile type providesinformation relating to an expected treatment outcome of a therapy. Insome embodiments, the MF profile indicates that a specific treatmentoption is recommended. In some embodiments, the MF profile indicatesthat a specific treatment option is non-curative. In some embodiments,the MF profile indicates that a specific treatment option is dependenton a certain feature of a tumor, for example, mutational status of thetumor.

In some embodiments, identifying a cancer as a 1^(st) MF profile typecancer indicates that a treatment selected from the group consisting ofan angiogenesis inhibitor, a CAFs inhibitor, an immunosuppressive factorinhibitor, a MDSC inhibitor, a Treg inhibitor, a metastatic activityinhibitor, and an immunotherapy should be recommended or used. In someembodiments, identifying a cancer as a 1^(st) MF profile type cancerindicates that treatment using a growth factor inhibitor dependent on acertain feature of a tumor (e.g., mutational status) should berecommended or used.

In some embodiments, identifying a cancer as a 2^(nd) MF profile typecancer indicates that a treatment selected from the group consisting ofan immunosuppressive factor inhibitor, a MDSC inhibitor, a Treginhibitor, a metastatic activity inhibitor, a checkpoint inhibitor, andan immunotherapy should be recommended or used. In some embodiments,identifying a cancer as a 2^(nd) MF profile type cancer indicates thattreatment using a growth factor inhibitor dependent on a certain featureof a tumor (e.g., mutational status) should be recommended or used.

In some embodiments, identifying a cancer as a 3^(rd) MF profile typeindicates that a treatment selected from the group consisting of anangiogenesis inhibitor, a CAFs inhibitor, an immunosuppressive factorinhibitor, a M2 macrophage inhibitor, a MDSC inhibitor, and a Treginhibitor should be recommended or used. In some embodiments,identifying a cancer as a 3^(rd) MF profile type indicates that acheckpoint inhibitor should be recommended or used.

In some embodiments, identifying a cancer as a 4^(th) MF profile typecancer indicates that a treatment such as an angiogenesis inhibitorand/or an immunotherapy should be recommended or used. In someembodiments, identifying a cancer as a 4^(th) MF profile type indicatesthat a non-curative treatment option may be selected from the groupconsisting of a kinase inhibitor, a radiotherapy, and a chemotherapy.

Visualization of MF Profiles

In some embodiments, a software program may provide a user with a visualrepresentation of a patient's MF profile and/or other informationrelated to a patient's cancer using an interactive graphical userinterface (GUI).

In response to being launched, the interactive GUI may provide the userof the software program with initial information related to a patient'scancer. Subsequently, the user may interact with the GUI to obtainadditional and/or alternative information related to a patient's cancer.FIGS. 3-37 show illustrative screenshots of the interactive graphicaluser interface and are described below.

FIG. 3 is a graphic illustrating different types of screens that may beshown to a user of the software program. Each of the different screensillustrated in FIG. 3 may be used to present different types ofinformation to the user. A screenshot of a control screen of thesoftware program is shown in the middle of FIG. 3. The control screenincludes portions for presenting information relating to treatmentselection, tumor properties, and clinical evidence of treatment efficacyand is described further with respect to FIGS. 7-37.

A user may interact with the control screen to obtain additionalinformation about, for example, immunotherapy selection, targetedtherapy selection, combination therapy design, tumor properties andtumor microenvironment, clinical evidence of targeted therapy efficacy,and clinical evidence of immunotherapy efficacy. The user may select aportion of the control screen (e.g., the immunotherapy portion) to viewone or more additional screens presenting information relating to theselected portion. As shown in FIG. 3, arrows point from a portion of thecontrol screen that may be selected toward the screens presentingadditional information related to the selected portion.

For example, the user may select the immunotherapy selection portion ofthe control screen to view one or more screens presenting informationrelating to various immunotherapies, biomarkers associated with animmunotherapy (e.g., genetic biomarkers, cellular biomarkers, andexpression biomarkers), immune cell properties of the patient's tumor,and clinical trials (e.g., information from and/or regarding publishedclinical trials and ongoing clinical trials).

In another example, the user may select the targeted therapy selectionportion of the control screen to view one or more screens presentinginformation relating to various targeted therapies, biomarkersassociated with targeted therapies (e.g., genetic biomarkers, cellularbiomarkers, and/or expression biomarkers), properties of the patient'stumor associated with the targeted therapy, and clinical trials (e.g.,published clinical trials and ongoing clinical trials).

In another example, the user may select the molecular-functionalportrait (MF profile) portion of the control screen to view one or morescreens presenting information relating to the patient's tumormicroenvironment. Such information may include information about tumorproperties (e.g., proliferation rate), angiogenesis, metastasis,cellular composition, cancer associated fibroblasts, pro-tumor immuneenvironment, and anti-tumor immune environment.

In yet another example, the user may select the clinical evidence oftreatment efficacy portion of the control screen to view one or morescreens presenting information relating to a therapy (e.g., animmunotherapy or targeted therapy). Such information may includedescription of the therapy, therapy efficacy, potential adverse effects,related publications, treatment regimen, and patient survival data.

A user of the software program may interact with the GUI to log into thesoftware program. FIG. 4 is a screenshot of the user's account profilescreen presented to the user in response to the user logging into thesoftware program. The user's account profile screen may provideinformation for one or more patients, such as patient identification anddiagnosis (e.g., Hugo27, Melanoma, Stage: IV) in a patient selectionportion (as shown in the upper left panel). The user's account profilescreen may also provide reports generated from the patient's informationby the software program in a report layout portion (as shown in theright panel). The report layout portion may provide the user withportions for viewing stored reports that were previously generated bythe software program or for creating a new report.

In response to selection by a user, a selected portion of the GUI may bevisually highlighted. As a set of non-limiting examples, a “visuallyhighlighted” element may be highlighted through a difference in font(e.g., by italicizing, bolding, and/or underlining), by surrounding thesection with a visual object (e.g., a box), by “popping” the element out(e.g., by increasing the zoom for that element), by changing the colorof an element, by shading the element, by incorporation of movement intothe element (e.g., by causing the element to move), any combination ofthe foregoing in a portion or the whole of the element, or in any othersuitable way.

If a user's account profile screen provides information about onepatient, the patient may be selected by the user to view a screenpresenting the patient's information. If a user's account profile screenprovides information about more than one patient, any one of thepatients may be selected by the user to view a screen presenting theselected patient's information. The user may select a stored report toview a screen presenting information relating to the selected report.The user may select the create new report portion to view a screen forcreating a new report. For example, the user may select the patientHugo27, as shown in the upper left panel.

FIG. 5 is a screenshot presenting the selected patient's informationprovided to the user in response to the user selecting the patient. Anoverview of the patient's information is presented in the patientoverview portion (as shown in the left panel) including clinicalcharacteristics of the patient's disease (e.g., histology report).Additional information about the patient or the patient's cancerincluding overall status, disease characteristics and generalrecommendations (as shown in the upper middle panel) is provided.Information relating to the selected patient's sequencing data ispresented in the Data Files portion (as shown in the right panel)including whole exome sequencing data (WES). The user may use the UploadData File portion of the screen to upload the patient's tumor biopsysequencing data.

FIG. 6 is a screenshot presenting that the patient's tumor biopsysequencing data was downloaded (as shown in the lower right panel). Theuser may select start in the launch analysis portion of the screen (asshown in the lower middle panel) to view a report created from thepatient's sequencing data and other information relating to the patientor the patient's cancer.

FIG. 7 is a screenshot presenting the selected patient's reportincluding information related to the patient's sequencing data, thepatient, and the patient's cancer. The therapy biomarkers portion (asshown in the left panel) presents information related to availabletherapies (e.g., immunotherapies and targeted therapies) and theirpredicted efficacy in the selected patient. Additional predictions ofthe efficacy of a therapy in the patient are provided in the machinepredictor portion and additional portion (as shown in the left panel).The MF profile portion presents information relating to the molecularcharacteristics of a tumor including tumor genetics, pro-tumormicroenvironment factors, and anti-tumor immune response factors (asshown in the middle panel). The clinical trials portion providesinformation relating to clinical trials (as shown in the right panel).The monotherapy or combinational therapy portion (as shown in the middlepanel) may be selected by the user to interactively design apersonalized treatment for a patient.

A user may select various portions of the screen to view additionalinformation. For example, a user may select anti-PD1 in theimmunotherapy biomarkers portion of the screen (as shown in the leftpanel) to view information relating to anti-PD1 treatment includingbiomarkers associated with anti-PD1 and tumor cell processes associatedwith anti-PD1 treatment.

FIG. 8 is a screenshot presenting information related to anti-PD1immunotherapy provided in response to selecting anti-PD1 immunotherapy(as shown by highlighting) in the immunotherapy biomarkers portion ofthe screen (as shown in the left panel). Information relating tobiomarkers associated with anti-PD1 immunotherapy is provided in thebiomarkers portion (as shown in the right panel). The biomarkers portionpresents genetic biomarkers, cellular biomarkers, and expressionbiomarkers, as well as patient specific information related to thosebiomarkers.

The user may select any one of the biomarkers presented in thebiomarkers markers portion to view additional information relating tothat biomarker including general information about the selectedbiomarker, patient specific information relating to the selectedbiomarker, information relating to tumor molecular processes associatedwith the selected biomarker, and treatment related informationassociated with the selected biomarker.

In response to selection by a user, the selected biomarker may behighlighted. FIG. 9 is a screenshot presenting the mutational burdenbiomarker (as shown by highlighting) was selected by the user. The usermay select another portion of the mutational burden biomarker to view ascreen presenting information relating to the mutational burdenbiomarker such as relevant publications.

FIG. 10 is a screenshot presenting information relating to themutational burden biomarker (as shown in the middle panel) provided inresponse to the user selecting the mutational burden biomarker. Theinformation may include a description of the biomarker, how thebiomarker was calculated, the patient's particular biomarker valuecompared to other patients (as shown in a histogram), and informationfrom publications relating to the selected biomarker.

Biomarkers are indicative of the molecular processes that take place inthe tumor microenvironment. Accordingly, a patient's biomarkers provideinformation specific to the patient's tumor microenvironment. The systemallows a user to interactively view biomarker information as it relatesto a molecular process in the tumor. Gene groups relating to tumormolecular processes associated with a particular biomarker arehighlighted in response to selecting that biomarker.

FIGS. 11-13 are screenshots demonstrating that tumor molecular processesgene groups presented in the MF profile that are associated with theselected biomarker are highlighted in response to the user selectingthat biomarker.

For example, the user may select the mutational burden biomarker whichis associated with the mutational status gene group and the neo-antigensload gene group in the tumor microenvironment. FIG. 11 is a screenshotpresenting that the mutational status gene group and neo-antigens loadgene group in the MF profile are highlighted in response to the userselecting the mutational burden biomarker (as shown in highlighting).

In another example, the user may select the CD8 T cells biomarker whichis associated with the T cells gene group in the tumor microenvironment.FIG. 12 is a screenshot presenting that the T cells gene group in the MFprofile is highlighted in response to the user selecting the CD8 T cellbiomarker (as shown in highlighting).

In yet another example, the user may select the PDL1 expressionbiomarker which is associated with the checkpoint inhibition gene groupin the tumor microenvironment. FIG. 13 is a screenshot presenting thatthe checkpoint inhibition gene group in the MF profile is highlighted inresponse to the user selecting the PDL1 expression biomarker.

The user may select a targeted therapy to view information relating totreatment with the selected targeted therapy including biomarkersassociated with the selected therapy and tumor cell processes associatedwith the selected therapy. For example, the user may select the targetedtherapy sunitinib.

FIG. 14 is a screenshot presenting information related to sunitinibtherapy provided in response to selecting sunitinib (as shown byhighlighting) in the targeted therapy biomarkers portion of the screen(as shown in the left panel). Information relating to biomarkersassociated with sunitinib therapy is provided in the biomarkers portion(as shown in the right panel). The biomarkers portion presents geneticbiomarkers, cellular biomarkers, and expression biomarkers, as well aspatient specific information related to those biomarkers.

Biomarkers are predictive of the efficacy of a therapy. Accordingly, apatient's biomarkers are predictive of the patient's response to atherapy. The system allows a user to interactively view biomarkerinformation as it relates to a predicted response to a therapy. Clinicalevidence of treatment efficacy for a therapy (e.g., an immunotherapy ora targeted therapy) may be interactively viewed by the user. FIGS. 15-18are screenshots demonstrating that a user may select a therapy to view ascreen presenting clinical trial data relating to the selected therapy.

For example, the user may select treatment with anti-PD1 immunotherapy.FIG. 15 is a screenshot presenting clinical trial data relating toanti-PD1 therapy effectivity in patients having stage IV metastaticmelanoma (as shown in the right panel) provided in response to the userselecting anti-PD1 immunotherapy (as shown in the left panel).

In another example, the user may select treatment with anti-CTLA4immunotherapy. FIG. 16 is a screenshot presenting clinical trial datarelating to anti-CTLA4 therapy effectivity in patients having stage IVmetastatic melanoma (as shown in the right panel) provided in responseto the user selecting anti-CTLA4 immunotherapy (as shown in the leftpanel).

A particular clinical trial can be selected to view further informationrelating to the clinical trial such as therapy efficacy, adverse effectsof the therapy, treatment regimen, and published results. FIG. 17 is ascreenshot presenting clinical trial data relating to the NCT01295827clinical trial of anti-PD1 treatment (as shown in the middle panel)provided in response to the user selecting the NCT01295827 clinicaltrial (as shown in the right panel).

A user can interactively view information relating to the clinicaltrial. For example, the user can minimize various portions ofinformation to view information in other portions. FIG. 18 is ascreenshot presenting the treatment regimen of the selected clinicaldata provided in response to the user minimizing the therapy classdescription and drug description portions. The screen may also presentinformation relating to ongoing clinical trials (marked by the letterA).

Information relating to a patient's tumor microenvironment is based onexpression of genes within the tumor microenvironment. The MF profile isa visual representation of gene groups within the tumor microenvironmentthat provide information about tumor properties, tumor processes (e.g.,angiogenesis), tumor immune environment, and cellular composition (e.g.,cancer associated fibroblasts). FIGS. 19-37 are screenshotsdemonstrating that a user may select portions of the MF profile to viewscreens presenting information related to the tumor microenvironment.

FIG. 19 is a screenshot presenting a patient's MF profile (as shown inthe middle panel). The MF profile may present any number of gene groups.As a non-limiting example, FIG. 19 presents five gene groups includingthe tumor properties gene group, angiogenesis gene group, cancerassociated fibroblasts gene group (the fibroblasts group), pro-tumorimmune environment gene group (tumor-promoting immune microenvironmentgroup), and anti-tumor immune environment gene group (anti-tumor immunemicroenvironment group). Any one of these gene groups may be selected toview a screen presenting additional gene groups associated with theselected gene group and information relating to the selected gene group.For example, a user may select the tumor properties gene group of the MFprofile to view additional gene groups associated with the tumorproperties gene group and information related to particular tumorproperties (e.g., tumor genetics and tumor cell properties).

FIG. 20 is a screenshot presenting additional gene groups associatedwith the tumor properties gene group provided to the user in response toselecting the tumor properties gene group. These gene groups includemutational status (mutation status) gene group, anti-metastatic(antimetastatic) factors gene group, metastatic factors (metastasissignature) gene group, tumor growth factors (growth factors) gene group,tumor suppressors gene group, oncogenes gene group (activated signalingpathways; including PI3K/AKT/mTOR signaling, RAS/RAF/MEK signaling, andReceptor tyrosine kinases expression), and proliferation rate genegroup. Information relating to tumor genetics (as shown in the leftpanel) and tumor cell properties (as shown in the right panel) areprovided in response to the user selecting the tumor properties genegroup. Each of the additional gene groups may be selected to viewinformation relating to the selected gene group. For example, a user mayselect the proliferation rate gene group in the MF profile.

FIG. 21 is a screenshot presenting information relating to the tumorproliferation rate (as shown in the right panel) provided in response tothe user selecting the tumor proliferation rate gene group (as shown inhighlighting) in the MF profile. The user may also view additionalinformation relating to properties of the patient's tumor.

The user may view different screens presenting information relating todifferent tumor properties such as a screen presenting informationrelated to tumor purity and tumor clone evolution. FIG. 22 is ascreenshot presenting information relating to the purity of thepatient's tumor in the tumor purity portion (as shown in the lower rightpanel) and information relating to the clonal evolution of the patient'stumor in the tumor clones evolution portion (as shown in the lower rightpanel).

The MF profile provides information relating to the pro-tumor immuneenvironment (tumor-promoting immune microenvironment), and anti-tumorimmune environment (anti-tumor immune microenvironment). For example,the user may select the anti-tumor immune environment (anti-tumor immunemicroenvironment) gene group in the MF profile to view informationrelating to the anti-tumor immune environment and the user may selectthe pro-tumor immune environment (tumor-promoting immunemicroenvironment) gene group in the MF profile to view informationrelating to the pro-tumor immune environment (tumor-promoting immunemicroenvironment).

FIG. 23 is a screenshot presenting information relating to theanti-tumor immune environment (as shown in the left panel; anti-tumorimmune microenvironment) provided in response to the user selecting theanti-tumor immune environment (anti-tumor immune microenvironment) genegroup and information relating to the pro-tumor immune environment (asshown in the right panel; tumor-promoting immune microenvironment) inresponse to the user selecting the pro-tumor immune environment(tumor-promoting immune microenvironment) gene group. Additional genegroups relating to the tumor microenvironment are presented in the MFprofile in response to selecting the anti-tumor and pro-tumor immuneenvironment (anti-tumor immune microenvironment and tumor-promotingimmune microenvironment) gene groups in the MF profile (as shown in themiddle panel).

Any one of these additional gene groups in the MF profile may beselected to view information relating to that gene group. For example,the user may select the T cells gene group in the MF profile. FIG. 24 isa screenshot presenting information relating to expression of genes thatdetermine T cell activity within the tumor in the anti-tumormicroenvironment portion (as shown in the lower left panel) provided inresponse to the user selecting the T cell gene group in the MF profile(as shown by highlighting).

FIG. 25 is a screenshot presenting information relating to expression ofgenes that determine cancer associated fibroblast activity within thetumor in the pro-tumor microenvironment (anti-tumor immunemicroenvironment) portion (as shown in the lower right panel) providedin response to the user selecting the cancer associated fibroblast(fibroblasts) gene group in the MF profile (as shown by highlighting).

The user may select portions in the anti-tumor immune environmentportion (as shown in the left panel; anti-tumor immune microenvironment)and the pro-tumor immune environment portion (as shown in the rightpanel; tumor-promoting immune microenvironment) to view additionalinformation relating to anti-tumor cells and pro-tumor (or tumorpromoting) cells within the tumor microenvironment (anti-tumor immunemicroenvironment and tumor-promoting immune microenvironment).

FIG. 26 is a screenshot presenting information relating to the number ofnon-malignant cells in the patient's tumor (as shown in the lower leftpanel) provided in response to the user selecting tumor infiltrate inthe anti-tumor immune environment portion (as shown in the upper leftpanel).

FIG. 27 is a screenshot presenting information relating to the TCRrepertoire in the patient's tumor (as shown in the lower right panel)provided in response to the user selecting tumor infiltrate in thepro-tumor immune environment portion (as shown in the upper right panel;tumor-promotive immune infiltrate).

As disclosed herein, the MF profile may present five gene groupsincluding the tumor properties gene group, angiogenesis gene group,cancer associated fibroblasts gene group (the fibroblasts group),pro-tumor immune environment gene group (tumor-promoting immunemicroenvironment group), and anti-tumor immune environment gene group(anti-tumor immune microenvironment group). Each of these gene groupsmay be selected by the user to view associated gene groups. When each ofthese gene groups is selected, the MF profile may present twenty-eightgene groups. A screen presenting a MF profile presenting twenty-eightgene groups (also described elsewhere herein) is shown in FIG. 28 (asshown in the middle panel).

The “combo therapy” (or “combination therapy”) portion can be used todesign a combination therapy based on one or more therapies. Combinationtherapies can be designed to target cancer (e.g., tumor) propertiespresented in the MF profile. For example, a combination therapyincluding a treatment to suppress pro-tumor process may be designed fora patient in which the MF profile showed active pro-tumor processes.

The combo therapy portion may present information to the user relatingto the selected therapy including a description of the selected therapy,gene groups targeted by the selected therapy, clinical data related tothe selected therapy, and predictions of the patient's response to theselected therapy based on information relating to the patient and thepatient's cancer. FIGS. 29-37 are screenshots demonstrating that a usermay interactively design a combination therapy using the combo therapyportion.

FIG. 29 is a screenshot presenting the combo therapy portion (as shownin the right panel) provided to the user in response to selecting thecombinational therapy portion (as shown in the middle panel).

FIG. 30 is a screenshot presenting anti-PD1 therapy incorporated intothe combo therapy portion (as shown in the upper right panel). Genegroups targeted by anti-PD1 therapy in the MF profile are marked witharrows. Information relating to the biological influence of anti-PD1therapy is presented in the therapy biological influence portion (asshown in the lower middle panel).

FIG. 31 is a screenshot presenting information related to sunitinibtreatment in the therapy biological influence portion (as shown in thelower middle panel) in response to the user selecting sunitinib in thetargeted therapy biomarkers portion (as shown by highlighting). The usermay determine whether the selected treatment should be incorporated intothe combination therapy based on this information.

FIG. 32 is a screenshot presenting sunitinib incorporation in the combotherapy portion in response to the user selecting sunitinib. Gene groupstargeted by the anti-PD1 and sunitinib combination therapy are markedwith arrows in the MF profile. Information relating to the combinationof anti-PD1 and sunitinib therapy is presented in the proposedeffectivity portion (as shown in the right panel) and in the potentialadverse effects portion (as shown in the right panel). Informationrelating to published and ongoing clinical trials matching the selectedcombination therapy are presented in the ongoing and finished combotrials portion (as shown in the right panel).

The combination therapy may include more than two therapies. Forexample, a user may add a vaccine therapy to the anti-PD1 and sunitinibcombination therapy designed by the user.

FIG. 33 is a screenshot presenting potential vaccine therapies such as apersonalized neo-antigenic vaccine and an off the shelf vaccine providedto the user in response to selecting vaccine in the immunotherapybiomarkers portion (as shown in the left panel).

FIG. 34 is a screenshot presenting information relating to treatmentwith a personalized neo-antigenic vaccine (as shown in the lower middlepanel) provided to the user in response to selecting a personalizedneo-antigenic vaccine (as shown by highlighting).

FIG. 35 is a screenshot presenting incorporation of a personalizedneo-antigenic vaccine in the combo therapy portion provided to the userin response to the user selecting the personalized neo-antigenicvaccine.

FIG. 36 is a screenshot presenting the personalized neo-antigenicvaccine therapy, anti-PD1 therapy, and sunitinib therapy in the combotherapy portion provided to the user in response to the userincorporating each of these therapies into the combo therapy portion.

Any one of the therapies in the combination therapy may be substitutedfor a different therapy. However, a particular combination therapy maybe inappropriate for a patient. In response to the user's design of aninappropriate combination therapy, the software will provide an alert tothe user indicating that the designed combo therapy is or may beinappropriate for the patient. The user may also receive an alert if thedesigned combination of therapies has a low effectivity score.

FIG. 37 is a screenshot presenting an alert that substitution ofsunitinib therapy with vemurafenib therapy is recognized by the softwareas an inappropriate combination for the patient.

Computer Implemented Methods for Generating, Visualizing and ClassifyingMF Profiles

Aspects of the technology described herein provide computer implementedmethods for generating, visualizing and classifying molecular-functional(MF) profiles of cancer patients.

In some embodiments, a software program may provide a user with a visualrepresentation of a patient's MF profile and/or other informationrelated to a patient's cancer using an interactive graphical userinterface (GUI). Such a software program may execute in any suitablecomputing environment including, but not limited to, a cloud-computingenvironment, a device co-located with a user (e.g., the user's laptop,desktop, smartphone, etc.), one or more devices remote from the user(e.g., one or more servers), etc.

For example, in some embodiments, the techniques described herein may beimplemented in the illustrative environment 200 shown in FIG. 2A. Asshown in FIG. 2A, within illustrative environment 200, one or morebiological samples of a patient 202 may be provided to a laboratory 204.Laboratory 204 may process the biological sample(s) to obtain expressiondata (e.g., DNA, RNA, and/or protein expression data) and provide it,via network 208, to at least one database 206 that stores informationabout patient 202.

Network 208 may be a wide area network (e.g., the Internet), a localarea network (e.g., a corporate Intranet), and/or any other suitabletype of network. Any of the devices shown in FIG. 2A may connect to thenetwork 208 using one or more wired links, one or more wireless links,and/or any suitable combination thereof.

In the illustrated embodiment of FIG. 2A, the at least one database 206may store expression data for the patient, medical history data for thepatient, test result data for the patient, and/or any other suitableinformation about the patient 202. Examples of stored test result datafor the patient include biopsy test results, imaging test results (e.g.,MRI results), and blood test results. The information stored in at leastone database 206 may be stored in any suitable format and/or using anysuitable data structure(s), as aspects of the technology describedherein are not limited in this respect. The at least one database 206may store data in any suitable way (e.g., one or more databases, one ormore files). The at least one database 206 may be a single database ormultiple databases.

As shown in FIG. 2A, illustrative environment 200 includes one or moreexternal databases 216, which may store information for patients otherthan patient 202. For example, external databases 216 may storeexpression data (of any suitable type) for one or more patients, medicalhistory data for one or more patients, test result data (e.g., imagingresults, biopsy results, blood test results) for one or more patients,demographic and/or biographic information for one or more patients,and/or any other suitable type of information. In some embodiments,external database(s) 216 may store information available in one or morepublically accessible databases such as TCGA (The Cancer Genome Atlas),one or more databases of clinical trial information, and/or one or moredatabases maintained by commercial sequencing suppliers. The externaldatabase(s) 216 may store such information in any suitable way using anysuitable hardware, as aspects of the technology described herein are notlimited in this respect.

In some embodiments, the at least one database 206 and the externaldatabase(s) 216 may be the same database, may be part of the samedatabase system, or may be physically co-located, as aspects of thetechnology described herein are not limited in this respect.

In some embodiments, information stored in patient information database206 and/or in external database(s) 216 may be used to perform any of thetechniques described herein related to determining whether a subject islikely to respond positively or not likely to respond positively to animmune checkpoint blockade therapy. For example, the information storedin the database(s) 206 and/or 216 may be accessed, via network 208, bysoftware executing on server(s) 210 to perform any one or more of thetechniques described herein including with reference to FIGS. 39A, 39B,39C, 39D, 40A and 40B.

For example, in some embodiments, server(s) 210 may access informationstored in database(s) 206 and/or 216 and use this information to performprocess 3900, described with reference to FIG. 39A, for identifying a MFprofile cluster with which to associate an MF profile for a subject.

As another example, in some embodiments, server(s) 210 may accessinformation stored in database(s) 206 and/or 216 and use thisinformation to perform process 3920, described with reference to FIG.39B, for generating MF profile clusters using RNA expression dataobtained from subjects having a particular type of cancer.

As another example, in some embodiments, server(s) 210 may accessinformation stored in database(s) 206 and/or 216 and use thisinformation to perform process 3940, described with reference to FIG.39C, for identifying an MF profile cluster with which to associate an MFprofile determined for a subject at least in part by determining thesubject's expression levels for multiple gene groups.

As another example, in some embodiments, server(s) 210 may accessinformation stored in database(s) 206 and/or 216 and use thisinformation to perform process 3960, described with reference to FIG.39D, for generating MF profile clusters using RNA expression dataobtained from subjects having a particular type of cancer, andassociating a subject with one of the generated MF clusters based on thesubject's MF profile.

As another example, in some embodiments, server(s) 210 may accessinformation stored in database(s) 206 and/or 216 and use thisinformation to perform process 4000, described with reference to FIG.40A, for generating an MF profile for a subject and generating an MFportrait for visualizing the MF profile in a graphical user interface.

As another example, in some embodiments, server(s) 210 may accessinformation stored in database(s) 206 and/or 216 and use thisinformation to perform process 4020, described with reference to FIG.40B, for presenting a generated personalized graphical user interface(GUI) to a user.

In some embodiments, server(s) 210 may include one or multiple computingdevices. When server(s) 210 include multiple computing devices, thedevice(s) may be physically co-located (e.g., in a single room) ordistributed across multi-physical locations. In some embodiments,server(s) 210 may be part of a cloud computing infrastructure. In someembodiments, one or more server(s) 210 may be co-located in a facilityoperated by an entity (e.g., a hospital, research institution) withwhich doctor 214 is affiliated. In such embodiments, it may be easier toallow server(s) 210 to access private medical data for the patient 202.

As shown in FIG. 2A, in some embodiments, the results of the analysisperformed by server(s) 210 may be provided to doctor 214 through acomputing device 214 (which may be a portable computing device, such asa laptop or smartphone, or a fixed computing device such as a desktopcomputer). The results may be provided in a written report, an e-mail, agraphical user interface, and/or any other suitable way. It should beappreciated that although in the embodiment of FIG. 2A, the results areprovided to a doctor, in other embodiments, the results of the analysismay be provided to patient 202 or a caretaker of patient 202, ahealthcare provider such as a nurse, or a person involved with aclinical trial.

In some embodiments, the results may be part of a graphical userinterface (GUI) presented to the doctor 214 via the computing device212. In some embodiments, the GUI may be presented to the user as partof a webpage displayed by a web browser executing on the computingdevice 212. In some embodiments, the GUI may be presented to the userusing an application program (different from a web-browser) executing onthe computing device 212. For example, in some embodiments, thecomputing device 212 may be a mobile device (e.g., a smartphone) and theGUI may be presented to the user via an application program (e.g., “anapp”) executing on the mobile device.

The GUI presented on computing device 212 provides a wide range ofoncological data relating to both the patient and the patient's cancerin a new way that is compact and highly informative. Previously,oncological data was obtained from multiple sources of data and atmultiple times making the process of obtaining such information costlyfrom both a time and financial perspective. Using the techniques andgraphical user interfaces illustrated herein, a user can access the sameamount of information at once with less demand on the user and with lessdemand on the computing resources needed to provide such information.Low demand on the user serves to reduce clinician errors associated withsearching various sources of information. Low demand on the computingresources serves to reduce processor power, network bandwidth, andmemory needed to provide a wide range of oncological data, which is animprovement in computing technology.

FIG. 2B shows a block diagram of an illustrative GUI 250 containinginformation about patient 202. GUI 250 may include separate portionsproviding different types of information about patient 202. IllustrativeGUI 150 includes the following portions: Patient Information Portion252, Molecular-Functional (MF) Portrait Portion 260, Clinical TrialInformation Portion 262, Immunotherapy Portion 254, Efficacy PredictorPortion 256, and Targeted Therapy Selection Portion 258.

Patient Information Portion 252 may provide general information aboutthe patient and the patient's cancer. General information about thepatient may include such information as the patient's name and date ofbirth, the patient's insurance provider, and contact information for thepatient such as address and phone number. General information about thepatient's cancer may include the patient's diagnosis, the patient'shistory of relapse and/or remission, and information relating to stageof the patient's cancer. Patient Information Portion 252 may alsoprovide information relating to potential treatment options for thepatient and/or previously administered treatments.

Molecular-Functional (MF) Portrait Portion 260 may include a molecularfunctional tumor portrait (MF profile) which refers to a graphicaldepiction of a tumor with regard to its molecular and cellularcomposition, and biological processes that are present within and/orsurrounding the tumor. Further aspects relating to a patient's MFprofile are provided herein.

Clinical Trial Information Portion 262 may include information relatingto a clinical trial for a therapy that may be and/or will beadministered to the patient. Clinical Trial Information Portion 262 mayprovide information about an ongoing clinical trial or a completedclinical trial. Information that may be provided in Clinical TrialInformation Portion 262 may include information related to a therapyused in the clinical trial such as dosage and dosage regimen, number anddiagnosis of patients participating in the clinical trial, and patientoutcomes.

Immunotherapy Portion 254 may include patient specific information as itrelates to an immunotherapy. Immunotherapy Portion 254 may provide suchinformation for different immunotherapies, for example, immunecheckpoint blockade therapies, anti-cancer vaccine therapies, and T celltherapies. Patient specific information relating to an immunotherapy mayinclude information about the patient such as the patient's biomarkersassociated with an immunotherapy and/or information about the patient'scancer such as composition of immune cells in the patient's tumor.

Efficacy Predictor Portion 256 may include information indicative of thepatient's predicted response to an immunotherapy based on patientspecific information presented in Immunotherapy Portion 254. EfficacyPredictor Portion 256 may provide predicted efficacy of an immunotherapydetermined, in some embodiments, using a patient's biomarkers asdescribed in International patent application number PCT/US18/37008,entitled “Systems and Methods for Identifying Cancer Treatments fromNormalized Biomarker Scores,” filed Jun. 12, 2018, the entire contentsof which are incorporated herein by reference. Additionally oralternatively, Efficacy Predictor Portion 256 may provide predictedefficacy of an immune checkpoint blockade therapy determined usingpatient specific information such as gene expression data as describedin International patent application number PCT/US18/37018, entitled“Systems and Methods for Identifying Responders and Non-Responders toImmune Checkpoint Blockade Therapy,” filed Jun. 12, 2018, the entirecontents of which are incorporated herein by reference.

Targeted Therapy Selection Portion 258 may include patient specificinformation as it relates to a targeted therapy. Targeted TherapySelection Portion 258 may provide such information for differenttargeted therapies, for example, a kinase inhibitor therapy, achemotherapy, and anti-cancer antibody therapy. Patient specificinformation relating to an a targeted therapy may include informationabout the patient such as the patient's biomarkers associated with atargeted therapy and/or information about the patient's cancer such aswhether a mutation is present in the patient's tumor.

An illustrative example of the graphical user interface 250 of FIG. 2Bis shown as graphical user interface 270 of FIG. 2C. As shown in FIG.2C, Patient Information Portion 272 may provide different information indifferent panels, for example, Overall Status panel, DiseaseCharacteristics panel, and General Recommendations panel. Overall Statuspanel, in some embodiments, may provide general information about thepatient such as patient name and patient age. Disease Characteristicspanel, in some embodiments, may provide information about the patient'scancer such as type of cancer and stage of cancer. GeneralRecommendations panel, in some embodiments, may provide previoustreatments and possible treatment options for the patient.

Clinical Trial Information Portion 282 a provides information relatingto a clinical trial for anti-PD1 therapy. Clinical Trial InformationPortion 282 a (as shown in the upper portion) shows a graph providingpatient overall response rate (ORR) for anti-PD1 therapy and othertherapies such as vaccine or IFNα therapies. A user may select portionsof the Clinical Trial Information Portion 282 a to access informationrelated to patient progression-free survival (PFS) and/or patientoverall survival (OS). Clinical Trial Information Portion 282 a (asshown in the lower portion) provides information relating to differentclinical trials that may be presented to a user including a briefdescription of the clinical trial.

Clinical Trial Information Portion 282 b provides information relatingto a clinical trial for different targeted therapies. Clinical TrialInformation Portion 282 b (as shown in the upper portion) shows a graphproviding patient overall response rate (ORR) for different targetedtherapies including sunitinib (SU), imatinib (IM), vemurafenib (VER) anddabrafenib (DAB). A user may select portions of the Clinical TrialInformation Portion 282 b to access information related to patientprogression-free survival (PFS) and/or patient overall survival (OS).Clinical Trial Information Portion 282 b (as shown in the lower portion)provides information relating to different clinical trials that may bepresented to a user including a brief description of the clinical trial.

Immunotherapy Portion 274 provides patient specific informationassociated with an immunotherapy and information indicative of thepatient's predicted response to that immunotherapy. ImmunotherapyPortion 274 provides such information for anti-PD1 therapy, atherapeutic cancer vaccine, IFNα therapy, IL2 therapy, anti-CTLA4therapy, and anti-angiogenic therapy. Patient specific information shownin Immunotherapy Portion 274 includes the patient's biomarkerinformation relating to various immunotherapies and the patient'stherapy scores calculated from their biomarkers.

Efficacy Predictor Portion 276 a provides information indicative of thepatient's predicted response to anti-PD1 therapy based on patientspecific information presented in Immunotherapy Portion 274. EfficacyPredictor Portion 276 b provides information indicative of the patient'spredicted response to anti-CTLA4 therapy based on patient specificinformation presented in Immunotherapy Portion 274.

Targeted Therapy Selection Portion 278 provides patient specificinformation associated with a targeted therapy and informationindicative of the patient's predicted response to the targeted therapy.Targeted Therapy Selection Portion 278 provides such information forsunitinib (SU), imatinib (IM), vemurafenib (VER), dabrafenib (DAB),trametinib, and pazopanib. Patient specific information shown inTargeted Therapy Selection Portion 278 includes a patient's biomarkerinformation relating to various targeted therapies and the patient'stherapy scores calculated from their biomarkers.

An illustrative implementation of a computer system 3800 that may beused in connection with any of the embodiments of the technologydescribed herein is shown in FIG. 38. The computer system 600 mayinclude one or more computer hardware processors 3800 and one or morearticles of manufacture that comprise non-transitory computer-readablestorage media (e.g., memory 3820 and one or more non-volatile storagedevices 3830). The processor(s) 3810 may control writing data to andreading data from the memory 3820 and the non-volatile storage device(s)3830 in any suitable manner. To perform any of the functionalitydescribed herein, the processor(s) 3810 may execute one or moreprocessor-executable instructions stored in one or more non-transitorycomputer-readable storage media (e.g., the memory 3820), which may serveas non-transitory computer-readable storage media storingprocessor-executable instructions for execution by the processor(s)3810.

Systems and methods described herein provide for calculating an MFprofile of a subject and associating the MF profile with an existing MFprofile cluster. For example, computer-implemented processes forcalculating a MF profile of a subject and associating the calculated MFprofile with an existing MF profile cluster are described with referenceto FIGS. 39A and 39C.

FIG. 39A is a flowchart of an illustrative computer-implemented process3900 for identifying a MF profile cluster with which to associate an MFprofile for a subject (e.g., a cancer patient), in accordance with someembodiments of the technology described herein. Process 3900 may beperformed by any suitable computing device(s). For example, may beperformed by a laptop computer, a desktop computer, one or more servers,in a cloud computing environment, or in any other suitable way.

Process 3900 begins at act 3902, where RNA expression data and/or wholeexome sequencing (WES) data for a subject is obtained. RNA expressiondata may be acquired using any method known in the art, e.g., wholetranscriptome sequencing, total RNA sequencing, and mRNA sequencing. Insome embodiments, obtaining RNA expression data and/or whole exomesequencing (WES) data comprises obtaining expression data from abiological sample from a patient and/or from a database storing suchexpression data. Further aspects relating to obtaining expression dataare provided in section titled “Obtaining Expression Data”.

Next, process 3900 proceeds to act 3904, where the MF profile for thesubject is determined by determining a set of expression levels for arespective set of gene groups that includes gene groups associated withcancer malignancy and cancer microenvironment. The MF profile may bedetermined for a subject having any type of cancer, including any of thetypes described herein. The MF profile may be determined using anynumber of gene groups that relate to compositions and processes presentwithin and/or surrounding the subject's tumor. Gene group expressionlevels, in some embodiments, may be calculated as a gene set enrichment(GSEA) score for the gene group. Further aspects relating to determiningMF profiles are provided in section titled “MF Profiles”.

Next, process 3900 proceeds to act 3906, where a MF profile cluster withwhich to associate the MF profile of the subject is identified. The MFprofile of the subject may be associated with any of the types of MFprofile clusters types described herein. A subject's MF profile may beassociated with one or multiple of the MF profile clusters in anysuitable way. For example, an MF profile may be associated with one ofthe MF profile clusters using a similarity metric (e.g., by associatingthe MF profile with the MF profile cluster whose centroid is closest tothe MF profile according to the similarity metric). As another example,a statistical classifier (e.g., k-means classifier or any other suitabletype of statistical classifier) may be trained to classify the MFprofile as belonging to one or multiple of the MF clusters. Furtheraspects relating to determining MF profiles are provided in section “MFProfiles”.

Optionally, process 3900 proceeds to act 3908, where a therapy for thesubject is identified based on the identified MF profile cluster. Theidentified therapy may be any type of anti-cancer therapy depending onthe patient's cancer and their identified MF profile cluster. A singleanti-cancer therapy or a combination of anti-cancer therapies may beidentified in act 3908. Identifying a therapy based on the MF profilecluster includes excluding those therapies that may be ineffective orharmful to the subject in order to identify a suitable therapy for thesubject. Further aspects related to using a patient's identified MFprofile cluster for clinical purposes are provided in section“Applications”.

The MF profile of the subject may be output to a user, in someembodiments, by displaying the MF profile to the user in a graphicaluser interface (GUI), including the information about the MF profile ina report, sending an email to the user, and/or in any other suitableway. For example, the MF profile of the subject and other patientrelated information may be provided to a user in a GUI as shown in FIGS.3-37.

In this way, a patient's MF profile can be identified and used forvarious clinical purposes including assessing the efficacy of atreatment for cancer and/or evaluating suitability of a patient forparticipating in a clinical trial.

FIG. 39C is a flowchart of an illustrative computer-implemented process3940 for identifying an existing MF profile cluster with which toassociate a MF profile for a subject (e.g., a cancer patient), inaccordance with some embodiments of the technology described herein.Process 3940 may be performed by any suitable computing device(s). Forexample, may be performed by a laptop computer, a desktop computer, oneor more servers, in a cloud computing environment, or in any othersuitable way.

Process 3940 begins at act 3942, where RNA expression data and/or wholeexome sequencing (WES) data for a subject having a particular type ofcancer is obtained. RNA expression data may be acquired using any methodknown in the art, e.g., whole transcriptome sequencing, total RNAsequencing, and mRNA sequencing. In some embodiments, obtaining RNAexpression data and/or whole exome sequencing (WES) data comprisesobtaining expression data from a biological sample from a patient and/orfrom a database storing such expression data. Further aspects relatingto obtaining expression data are provided in section “ObtainingExpression Data”.

Next, process 3940 proceeds to act 3944, where the MF profile for thesubject is determined by determining a set of expression levels for arespective set of gene groups that includes at least one gene groupassociated with cancer malignancy and at least four gene groupsassociated with cancer microenvironment. The at least one gene groupassociated with cancer malignancy, in some embodiments, consists of atumor properties gene group. The at least four gene groups associatedwith cancer microenvironment, in some embodiments, consists oftumor-promoting immune microenvironment group, anti-tumor immunemicroenvironment group, angiogenesis group, and fibroblasts group.

It should be appreciated that act 3944 may be performed using any numberof gene groups associated with cancer malignancy and cancermicroenvironment. For example, MF profiles may be determined using setof gene groups that includes 19 gene groups where the gene groupsassociated with cancer malignancy consists of the proliferation rategroup, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signalinggroup, the receptor tyrosine kinases expression group, the tumorsuppressors group, the metastasis signature group, the anti-metastaticfactors group, and the mutation status group, and the gene groupsassociated with cancer microenvironment consists of the antigenpresentation group, the cytotoxic T and NK cells group, the B cellsgroup, the anti-tumor microenvironment group, the checkpoint inhibitiongroup, the Treg group, the MDSC group, the granulocytes group, thecancer associated fibroblasts group, the angiogenesis group, and thetumor-promotive immune group.

In another example, MF profiles may be determined using set of genegroups that includes 30 gene groups where the gene groups associatedwith cancer malignancy consists of the proliferation rate group, thePI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, thereceptor tyrosine kinases expression group, the growth factors group,the tumor suppressors group, the metastasis signature group, theanti-metastatic factors group, and the mutation status group, and thegene groups associated with cancer microenvironment consists of the MHCIgroup, the MHCII group, the coactivation molecules group, the effectorcells group, the NK cells group, the T cell traffic group, the T cellsgroup, the B cells group, the M1 signatures group, the Th1 signaturegroup, the antitumor cytokines group, the checkpoint inhibition group,the Treg group, the MDSC group, the granulocytes group, the M2 signaturegroup, the Th2 signature group, the protumor cytokines group, the cancerassociated fibroblasts group, the angiogenesis group, and the complementinhibition group.

The MF profile may be determined using any number of gene groups (orfunctional modules) that relate to compositions and processes presentwithin and/or surrounding the subject's tumor. Gene groups may compriseany number of genes and may be related to any composition and process.Further aspects relating to the gene groups are provided in section “MFProfile Modules”. Gene group expression levels, in some embodiments, maybe calculated as a gene set enrichment (GSEA) score for the gene group.Further aspects relating to determining MF profiles are provided insection “MF Profiles”.

Next, process 3940 proceeds to act 3946, where information specifying MFprofile clusters for the particular cancer type are accessed. DifferentMF profile clusters are accessed for different cancers. For example, MFprofile clusters associated with lung cancer are accessed when process3940 is performed for a patient having lung cancer and MF profileclusters associated with melanoma are accessed when process 3940 isperformed for a patient having melanoma. Any number of MF profileclusters for the particular cancer may be accessed including at leasttwo, at least 5, at least 10 or at least 20. The number of accessed MFprofiles, in some embodiments, may be between 2-20, between 2-10, orbetween 15-20. The number of accessed MF profile clusters may varydepending on the particular cancer with which the MF profile clustersare associated. For example, 5 MF profile clusters may be accessed whenthe particular cancer type is lung cancer and 12 MF profile clusters maybe accessed when the particular cancer is melanoma. Accessinginformation specifying MF profile clusters for the particular cancer mayinclude accessing information from a variety of sources and/or a varietyof databases.

Next, process 3940 proceeds to act 3948, where a MF profile cluster withwhich to associate the MF profile of the subject is identified. The MFprofile of the subject may be associated with any of the types of MFprofile clusters types described herein. A subject's MF profile may beassociated with one or multiple of the MF profile clusters in anysuitable way. For example, an MF profile may be associated with one ofthe MF profile clusters using a similarity metric (e.g., by associatingthe MF profile with the MF profile cluster whose centroid is closest tothe MF profile according to the similarity metric). As another example,a statistical classifier (e.g., k-means classifier or any other suitabletype of statistical classifier) may be trained to classify the MFprofile as belonging to one or multiple of the MF clusters. Furtheraspects relating to determining MF profiles are provided in section “MFProfiles”.

The MF profile of the subject may be output to a user, in someembodiments, by displaying the MF profile to the user in a graphicaluser interface (GUI), including the information about the MF profile ina report, sending an email to the user, and/or in any other suitableway. For example, the MF profile of the subject and other patientrelated information may be provided to a user in a GUI as shown in FIGS.3-37.

In this way, a patient's MF profile can be identified and used forvarious clinical purposes including assessing the efficacy of atreatment for cancer and/or evaluating suitability of a patient forparticipating in a clinical trial.

Systems and methods described herein provide for generating MF profileclusters and for generating a MF profile for a patient and associatingthat MF profile to a generated MF cluster. For example, acomputer-implemented process 3920 for generating MF profile clustersusing RNA expression data obtained from subjects having a particulartype of cancer is described with reference to FIG. 39B. As anotherexample, a computer-implemented process 3960 for generating MF profileclusters using RNA expression data obtained from subjects having aparticular type of cancer, and associating a subject with one of thegenerated MF clusters based on the subject's MF profile is describedwith reference to FIG. 39D.

FIG. 39B is a flowchart of an illustrative computer-implemented process3920 for generating MF profile clusters using expression data obtainedfrom subjects having a particular type of cancer, in accordance withsome embodiments of the technology described herein. MF profile clustersmay be generated for any cancer using expression data obtained frompatients having that type of cancer. For example MF profile clustersassociated with melanoma may be generated using expression data frommelanoma patients. In another example MF profile clusters associatedwith lung cancer may be generated using expression data from lung cancerpatients.

Process 3920 begins at act 3922, where RNA expression data and/or wholeexome sequencing (WES) data for a plurality of subjects having aparticular cancer are obtained. The plurality of subjects for whichexpression data is obtained may comprise any number of patients having aparticular cancer. For example, expression data may be obtained for aplurality of melanoma patients, for example, 100 melanoma patients, 1000melanoma patients, or any number of melanoma patients as the technologyis not so limited. RNA expression data may be acquired using any methodknown in the art, e.g., whole transcriptome sequencing, total RNAsequencing, and mRNA sequencing. Further aspects relating to obtainingexpression data are provided in section “Obtaining Expression Data”.

Next, process 3920 proceeds to act 3924, where the MF profile for eachsubject in the plurality of subject is determined by determining a setof expression levels for a respective set of gene groups that includesgene groups associated with cancer malignancy and cancermicroenvironment. MF profiles may be determined using any number of genegroups that relate to compositions and processes present within and/orsurrounding the subject's tumor. Gene group expression levels, in someembodiments, may be calculated as a gene set enrichment (GSEA) score forthe gene group. Further aspects relating to determining MF profiles areprovided in section titled “MF Profiles”.

Next, process 3920 proceeds to act 3926, where the plurality of MFprofiles are clustered to obtain MF profile clusters. MF profiles may beclustered using any of the techniques described herein including, forexample, community detection clustering, dense clustering, k-meansclustering, or hierarchical clustering. MF profiles may be clustered forany type of cancer using MF profiles generated for patients having thattype of cancer. MF profile clusters, in some embodiments, comprises a1^(st) MF profile cluster, a 2^(nd) MF profile cluster, a 3^(rd) MFprofile, and a 4^(th) MF profile. The relative sizes of 1^(st)-4^(th) MFclusters may vary among cancer types. For example, the size of the3^(rd) MF profile cluster (shown as C) was larger for ACC(adrenocortical carcinoma) than that of BLCA (bladder urothelialcarcinoma. MF profiles were clustered for different cancers as shown inExample 4. Further aspects relating to MF profile clusters are providedin section titled “MF profiles”.

Next, process 3920 proceeds to act 3928, where the plurality of MFprofiles in association with information identifying the particularcancer type are stored. MF profiles may be stored in a database in anysuitable format and/or using any suitable data structure(s), as aspectsof the technology described herein are not limited in this respect. Thedatabase may store data in any suitable way, for example, one or moredatabases and/or one or more files. The database may be a singledatabase or multiple databases.

In this way, MF profile clusters can be stored and used as existing MFprofile clusters with which a patient's MF profile can be associated.Existing MF profiles clusters, in some embodiments, may be associatedwith a patient's MF profile generated using five gene groups, 19 genegroups, or 30 gene groups as described with respect to FIG. 39C.

FIG. 39D is a flowchart of an illustrative computer-implemented process3960 for generating MF profile clusters using expression data obtainedfrom subjects having a particular type of cancer, and associating asubject with one of the generated MF clusters based on the subject's MFprofile, in accordance with some embodiments of the technology describedherein. Process 3960 may be performed by any suitable computingdevice(s). For example, may be performed by a laptop computer, a desktopcomputer, one or more servers, in a cloud computing environment, or inany other suitable way.

Process 3960 begins at act 3962, where RNA expression data and/or wholeexome sequencing (WES) data for each subject in a plurality of subjectshaving a particular type of cancer is obtained. RNA expression data maybe acquired using any method known in the art, e.g., whole transcriptomesequencing, total RNA sequencing, and mRNA sequencing. In someembodiments, obtaining RNA expression data and/or whole exome sequencing(WES) data comprises obtaining expression data from a biological samplefrom a patient and/or from a database storing such expression data.Further aspects relating to obtaining expression data are provided insection “Obtaining Expression Data”.

Next, process 3960 proceeds to act 3964, where the MF profile for eachsubject in the plurality of subjects is determined by determining a setof expression levels for a respective set of gene groups that includesat least one gene group associated with cancer malignancy and at leastfour gene groups associated with cancer microenvironment. The MF profilemay be determined using any number of gene groups (or functionalmodules) that relate to compositions and processes present within and/orsurrounding the subject's tumor. Gene groups may comprise any number ofgenes and may be related to any composition and process. Further aspectsrelating to the gene groups are provided in section “MF ProfileModules”. Gene group expression levels, in some embodiments, may becalculated as a gene set enrichment (GSEA) score for the gene group.Further aspects relating to determining MF profiles are provided insection “MF Profiles”.

Next, process 3960 proceeds to act 3966, where the plurality of MFprofiles are clustered to obtain MF profile clusters. MF profiles may beclustered using any of the techniques described herein including, forexample, community detection clustering, dense clustering, k-meansclustering, or hierarchical clustering. MF profiles may be clustered forany type of cancer using MF profiles generated for patients having thattype of cancer. MF profile clusters, in some embodiments, comprises a1^(st) MF profile cluster, a 2^(nd) MF profile cluster, a 3^(rd) MFprofile, and a 4^(th) MF profile. The relative sizes of 1^(st)-4^(th) MFclusters may vary among cancer types. For example, the size of the3^(rd) MF profile cluster (shown as C) was larger for ACC(adrenocortical carcinoma) than that of BLCA (bladder urothelialcarcinoma. MF profiles were clustered for different cancers as shown inExample 4. Further aspects relating to MF profile clusters are providedin section titled “MF profiles”.

Next, process 3960 proceeds to act 3968, where RNA expression dataand/or whole exome sequencing (WES) data for an additional subject isobtained. Expression data for an additional subject may be obtained byany suitable means as described in further detail in section “ObtainingExpression Data”. Expression data for the additional subject may beobtained in the same manner used for obtaining expression data of theplurality of subjects. Alternatively or in addition to, expression datafor the additional subject may be obtained in a manner different fromthat used to obtain expression data of the plurality of subjects.Further aspects relating to obtaining expression data are provided insection “Obtaining Expression Data”.

Next, process 3960 proceeds to act 3970, where MF profiles for theadditional subject are determined using the additional subject'sexpression data. The MF profile for the additional subject is determinedby determining a set of expression levels for a respective set of genegroups that includes at least one gene group associated with cancermalignancy and at least four gene groups associated with cancermicroenvironment. The MF profile may be determined using any number ofgene groups (or functional modules) that relate to compositions andprocesses present within and/or surrounding the subject's tumor. Genegroups may comprise any number of genes and may be related to anycomposition and process. Further aspects relating to the gene groups areprovided in section “MF Profile Modules”. Gene group expression levels,in some embodiments, may be calculated as a gene set enrichment (GSEA)score for the gene group. Further aspects relating to determining MFprofiles are provided in section “MF Profiles”.

Next, process 3960 proceeds to act 3972, where a MF profile cluster withwhich to associate the MF profile of the subject is identified. The MFprofile of the subject may be associated with any of the types of MFprofile clusters determined in act 3966. A subject's MF profile may beassociated with one or multiple of the MF profile clusters in anysuitable way. For example, an MF profile may be associated with one ofthe MF profile clusters using a similarity metric (e.g., by associatingthe MF profile with the MF profile cluster whose centroid is closest tothe MF profile according to the similarity metric). As another example,a statistical classifier (e.g., k-means classifier or any other suitabletype of statistical classifier) may be trained to classify the MFprofile as belonging to one or multiple of the MF clusters. Furtheraspects relating to determining MF profiles are provided in section “MFProfiles”.

Optionally, process 3960 proceeds to act 3974, where a therapy for thesubject is identified based on the identified MF profile cluster. Theidentified therapy may be any type of anti-cancer therapy depending onthe patient's cancer and their identified MF profile cluster. A singleanti-cancer therapy or a combination of anti-cancer therapies may beidentified in act 3974. Identifying a therapy based on the MF profilecluster includes excluding those therapies that may be ineffective orharmful to the subject in order to identify a suitable therapy for thesubject. Further aspects related to using a patient's identified MFprofile cluster for clinical purposes are provided in section“Applications”.

The MF profile of the subject may be output to a user, in someembodiments, by displaying the MF profile to the user in a graphicaluser interface (GUI), including the information about the MF profile ina report, sending an email to the user, and/or in any other suitableway. For example, the MF profile of the subject and other patientrelated information may be provided to a user in a GUI as shown in FIGS.3-37.

In this way, a patient's MF profile can be identified and used forvarious clinical purposes including assessing the efficacy of atreatment for cancer and/or evaluating suitability of a patient forparticipating in a clinical trial.

Systems and methods described herein provide for generating a MF profilefor a patient and generating a visualization of the generated MF profileas a MF portrait. For example, a computer-implemented process forgenerating a MF profile and an associated MF portrait is shown in FIG.40A, and a computer-implemented process for generating a MF profileusing five gene groups and an associated MF portrait is shown in FIG.40B.

FIG. 40A is a flowchart of an illustrative computer-implemented process4000 for generating a MF profile and an associated MF portrait, inaccordance with some embodiments of the technology described herein.Process 4000 may be performed by any suitable computing device(s). Forexample, may be performed by a laptop computer, a desktop computer, oneor more servers, in a cloud computing environment, or in any othersuitable way.

Process 4000 begins at act 4002, where RNA expression data and/or wholeexome sequencing (WES) data for a subject having a particular type ofcancer is obtained. RNA expression data may be acquired using any methodknown in the art, e.g., whole transcriptome sequencing, total RNAsequencing, and mRNA sequencing. In some embodiments, obtaining RNAexpression data and/or whole exome sequencing (WES) data comprisesobtaining expression data from a biological sample from a patient and/orfrom a database storing such expression data. Further aspects relatingto obtaining expression data are provided in section “ObtainingExpression Data”.

Next, process 4000 proceeds to act 4004, where the MF profile for thesubject is determined by determining a set of expression levels for arespective set of gene groups that includes gene groups associated withcancer malignancy and gene groups associated with cancermicroenvironment. The MF profile may be determined for a subject havingany type of cancer, including any of the types described herein. The MFprofile may be determined using any number of gene groups (or functionalmodules) that relate to compositions and processes present within and/orsurrounding the subject's tumor. Gene group expression levels, in someembodiments, are calculated as a gene set enrichment (GSEA) score forthe gene group. Further aspects relating to determining MF profiles areprovided in section “MF Profiles”.

Next, process 4000 proceeds to act 4006, where a first set of visualcharacteristics for a first plurality of GUI elements using the firstgene group expression levels are determined. Examples of visualcharacteristics for a GUI element include color, shading or pattern,size, and/or shape. A set of visual characteristics may contain anynumber of visual characteristics. GUI elements, for example, includegenes, gene groups, biomarkers, and biomarker information. A pluralityof GUI elements may contain any number of GUI elements. Further aspectsof visual characteristics and GUI elements are shown in and/or describedwith reference to FIGS. 3-37.

Next, process 4000 proceeds to act 4008, where a second set of visualcharacteristics for a second plurality of GUI elements using the secondgene group expression levels are determined. Examples of visualcharacteristics for a GUI element include color, shading or pattern,size, and/or shape. A set of visual characteristics may contain anynumber of visual characteristics. GUI elements, for example, includegenes, gene groups, biomarkers, and biomarker information. A pluralityof GUI elements may contain any number of GUI elements. Further aspectsof visual characteristics and GUI elements are shown in and/or describedwith reference to FIGS. 3-37.

Next, process 4000 proceeds to act 4010, where a GUI containing a firstportion including the first GUI element and a second portion includingthe second GUI element is generated. For example, the MF profile of thesubject and other patient related information may be provided to a userin a GUI as shown in FIGS. 3-37. Further aspects relating to the GUI asshown in FIGS. 3-37 are provided in section “Visualization of MFProfiles”.

Next, process 4000 proceeds to act 4012, where the generatedpersonalized GUI is presented to a user. In some embodiments, the GUImay be presented to the user as part of a webpage displayed by a webbrowser. In some embodiments, the GUI may be presented to the user usingan application program (different from a web-browser). For example, insome embodiments, the GUI may be presented to the user via anapplication program (e.g., “an app”) executing on a mobile device.

FIG. 40B is a flowchart of an illustrative computer-implemented process4020 for generating a MF profile using at least one gene groupassociated with cancer malignancy and at least four gene groupsassociated with cancer microenvironment, and an associated MF portrait,in accordance with some embodiments of the technology described herein.Process 4020 may be performed by any suitable computing device(s). Forexample, may be performed by a laptop computer, a desktop computer, oneor more servers, in a cloud computing environment, or in any othersuitable way.

Process 4020 begins at act 4022, where RNA expression data and/or wholeexome sequencing (WES) data for a subject having a particular type ofcancer is obtained. RNA expression data may be acquired using any methodknown in the art, e.g., whole transcriptome sequencing, total RNAsequencing, and mRNA sequencing. In some embodiments, obtaining RNAexpression data and/or whole exome sequencing (WES) data comprisesobtaining expression data from a biological sample from a patient and/orfrom a database storing such expression data. Further aspects relatingto obtaining expression data are provided in section “ObtainingExpression Data”.

Next, process 4020 proceeds to act 4024, where the MF profile for thesubject is determined by determining a set of expression levels for arespective set of gene groups that includes at least one gene groupassociated with cancer malignancy and at least four gene groupsassociated with cancer microenvironment. The at least one gene groupassociated with cancer malignancy, in some embodiments, consists of atumor properties gene group. The at least four gene groups associatedwith cancer microenvironment, in some embodiments, consists oftumor-promoting immune microenvironment group, anti-tumor immunemicroenvironment group, angiogenesis group, and fibroblasts group.

It should be appreciated that act 4024 may be performed using any numberof gene groups associated with cancer malignancy and cancermicroenvironment. For example, MF profiles may be determined using setof gene groups that includes 19 gene groups where the gene groupsassociated with cancer malignancy consists of the proliferation rategroup, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signalinggroup, the receptor tyrosine kinases expression group, the tumorsuppressors group, the metastasis signature group, the anti-metastaticfactors group, and the mutation status group, and the gene groupsassociated with cancer microenvironment consists of the antigenpresentation group, the cytotoxic T and NK cells group, the B cellsgroup, the anti-tumor microenvironment group, the checkpoint inhibitiongroup, the Treg group, the MDSC group, the granulocytes group, thecancer associated fibroblasts group, the angiogenesis group, and thetumor-promotive immune group.

In another example, MF profiles may be determined using set of genegroups that includes 30 gene groups where the gene groups associatedwith cancer malignancy consists of the proliferation rate group, thePI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, thereceptor tyrosine kinases expression group, the growth factors group,the tumor suppressors group, the metastasis signature group, theanti-metastatic factors group, and the mutation status group, and thegene groups associated with cancer microenvironment consists of the MHCIgroup, the MHCII group, the coactivation molecules group, the effectorcells group, the NK cells group, the T cell traffic group, the T cellsgroup, the B cells group, the M1 signatures group, the Th1 signaturegroup, the antitumor cytokines group, the checkpoint inhibition group,the Treg group, the MDSC group, the granulocytes group, the M2 signaturegroup, the Th2 signature group, the protumor cytokines group, the cancerassociated fibroblasts group, the angiogenesis group, and the complementinhibition group.

The MF profile may be determined using any number of gene groups (orfunctional modules) that relate to compositions and processes presentwithin and/or surrounding the subject's tumor. Gene groups may compriseany number of genes and may be related to any composition and process.Further aspects relating to the gene groups are provided in section “MFProfile Modules”. Gene group expression levels, in some embodiments, maybe calculated as a gene set enrichment (GSEA) score for the gene group.Further aspects relating to determining MF profiles are provided insection “MF Profiles”.

Next, process 4020 proceeds to act 4026, where a first set of visualcharacteristics for a first plurality of GUI elements using the firstgene group expression levels are determined. Examples of visualcharacteristics for a GUI element include color, shading or pattern,size, and/or shape. A set of visual characteristics may contain anynumber of visual characteristics. GUI elements, for example, includegenes, gene groups, biomarkers, and biomarker information. A pluralityof GUI elements may contain any number of GUI elements. Further aspectsof visual characteristics and GUI elements are shown in and/or describedwith reference to FIGS. 3-37.

Next, process 4020 proceeds to act 4028, where a second set of visualcharacteristics for a second plurality of GUI elements using the secondgene group expression levels are determined. Examples of visualcharacteristics for a GUI element include color, shading or pattern,size, and/or shape. A set of visual characteristics may contain anynumber of visual characteristics. GUI elements, for example, includegenes, gene groups, biomarkers, and biomarker information. A pluralityof GUI elements may contain any number of GUI elements. Further aspectsof visual characteristics and GUI elements are shown in and/or describedwith reference to FIGS. 3-37.

Next, process 4020 proceeds to act 4030, where a GUI containing a firstportion including the first GUI element and a second portion includingthe second GUI element is generated. For example, the MF profile of thesubject and other patient related information may be provided to a userin a GUI as shown in FIGS. 3-37. Further aspects relating to the GUI asshown in FIGS. 3-37 are provided in section “Visualization of MFProfiles”.

Next, process 4020 proceeds to act 4032, where the generatedpersonalized GUI is presented to a user. In some embodiments, the GUImay be presented to the user as part of a webpage displayed by a webbrowser. In some embodiments, the GUI may be presented to the user usingan application program (different from a web-browser). For example, insome embodiments, the GUI may be presented to the user via anapplication program (e.g., “an app”) executing on a mobile device.

Such MF portraits provided in the GUI can used for various clinicalpurposes described herein including assessing the efficacy of atreatment for cancer and/or evaluating suitability of a patient forparticipating in a clinical trial.

Applications

Methods and compositions for tumor type characterization as describedherein may be used for various clinical purposes including, but notlimited to, monitoring the progress of cancer in a subject, assessingthe efficacy of a treatment for cancer, identifying patients suitablefor a particular treatment, evaluating suitability of a patient forparticipating in a clinical trial and/or predicting relapse in asubject. Accordingly, described herein are diagnostic and prognosticmethods for cancer treatment based on tumor type described herein.

Methods and compositions described herein can be used to evaluate theefficacy of a cancer treatment, such as those described herein, giventhe correlation between cancer type (e.g., tumor types) and cancerprognosis. For example, multiple biological samples, such as thosedescribed herein, can be collected from a subject to whom a treatment isperformed either before and after the treatment or during the course ofthe treatment. The cancer type (e.g., the tumor type) in the biologicalsample from the subject can be determined using any of the methodsdescribed herein. For example, if the cancer type indicates that thesubject has a poor prognosis and the cancer type changes to a cancertype indicative of a favorable prognosis after the treatment or over thecourse of treatment (e.g., 1^(st) MF profile cancer type in a latercollected sample when compared to 4^(th) MF profile cancer type in anearlier collected sample), it indicates that the treatment is effective.

If the subject is identified as not responsive to the treatment based oncancer type (e.g., no change in cancer type is identified in response totreatment), a higher dose and/or greater frequency of dosage of theanti-cancer therapeutic agent may be administered to the identifiedsubject. Alternatively, an alternative treatment can be administered toa subject who is found to not be responsive to a first or subsequenttreatment. In some embodiments, the dosage or frequency of dosage of thetherapeutic agent is maintained, lowered, or ceased in a subjectidentified as responsive to the treatment or not in need of furthertreatment. In certain embodiments, the dosage or frequency of dosage ofthe therapeutic agent is increased in a subject identified asnon-responsive to the treatment. In some embodiments, a firsttherapeutic agent is halted and a new (second) therapeutic is used totreat the subject; or (alternatively) an additional (second) therapeuticis added in a subject identified as non-responsive to the firsttherapeutic agent.

In some embodiments, cancer types can also be used to identify a cancerthat may be treatable using a specific anti-cancer therapeutic agent(e.g., a chemotherapy). To practice this method, the cancer type in asample (e.g., a tumor biopsy) collected from a subject having cancer canbe determined using methods described herein. If the cancer type isidentified as being susceptible to treatment with an anti-cancertherapeutic agent, the method may further comprise administering to thesubject having the cancer an effective amount of the anti-cancertherapeutic agent.

In some embodiments, the methods and compositions for cancer typecharacterization as described herein may be relied on in the developmentof new therapeutics for cancer. In some embodiments, the cancer type mayindicate or predict the efficacy of a new therapeutic or the progressionof cancer in a subject prior to, during, or after the administration ofthe new therapy.

In some embodiments, methods and compositions for cancer typecharacterization as described herein may be used to evaluate suitabilityof a patient for participating in a clinicial trial. In someembodiments, the cancer type may be used to include patients in aclinical trial. In some embodiments, patients having a specified cancertype (e.g., type A, or 1^(st) MF profile) are included in a clinicaltrial. Herein, cancer types A-D correspond to the 1^(st)-4^(th) MFprofile types, respectively. In some embodiments, patients having anyone of two specified cancer types (e.g., 1^(st) MF profile or 4^(th) MFprofile) are included in a clinical trial. In some embodiments, patientshaving any one of three specified cancer types (e.g., patients having a1^(st) MF profile, a 2^(nd) MF profile, or a 3^(rd) MF profile) areincluded in a clinical trial. In some embodiments, patients having anyone of four specified cancer types (e.g., patients having a 1^(st) MFprofile, a 2^(nd) MF profile, a 3^(rd) MF profile, or a 4^(th) MFprofile) are included in a clinical trial.

In some embodiments, the cancer type may be used to exclude patients ina clinical trial. In some embodiments, patients having a specifiedcancer type (e.g., 1^(st) MF profile) are excluded from a clinicaltrial. In some embodiments, patients having any one of two specifiedcancer types (e.g., 1^(st) MF profile or 4^(th) MF profile) are excludedfrom a clinical trial. In some embodiments, patients having any one ofthree specified cancer types (e.g., patients having a 1^(st) MF profile,a 2^(nd) MF profile, or a 3^(rd) MF profile) are excluded from aclinical trial. In some embodiments, patients having any one of fourspecified cancer types (e.g., patients having a 1^(st) MF profile, a2^(nd) MF profile, a 3^(rd) MF profile, or a 4^(th) MF profile) areexcluded from a clinical trial.

Further, methods and compositions for tumor type characterization asdescribed herein may be applied for non-clinical uses including, forexample, for research purposes. In some embodiments, the methodsdescribed herein may be used to study cancer cell function. For example,the methods described herein may be used to evaluate a tumor process(e.g., tumor metastasis), which can be used for various purposesincluding identifying targets that specifically effect the tumor processbeing evaluated.

Methods of Treatment

In certain methods described herein, an effective amount of anti-cancertherapy described herein may be administered or recommended foradministration to a subject (e.g., a human) in need of the treatment viaa suitable route (e.g., intravenous administration).

The subject to be treated by the methods described herein may be a humanpatient having, suspected of having, or at risk for a cancer. Examplesof a cancer include, but are not limited to, melanoma, lung cancer,brain cancer, breast cancer, colorectal cancer, pancreatic cancer, livercancer, prostate cancer, skin cancer, kidney cancer, bladder cancer, orprostate cancer. The subject to be treated by the methods describedherein may be a mammal (e.g., may be a human). Mammals include, but arenot limited to: farm animals (e.g., livestock), sport animals,laboratory animals, pets, primates, horses, dogs, cats, mice, and rats.

A subject having a cancer may be identified by routine medicalexamination, e.g., laboratory tests, biopsy, PET scans, CT scans, orultrasounds. A subject suspected of having a cancer might show one ormore symptoms of the disorder, e.g., unexplained weight loss, fever,fatigue, cough, pain, skin changes, unusual bleeding or discharge,and/or thickening or lumps in parts of the body. A subject at risk for acancer may be a subject having one or more of the risk factors for thatdisorder. For example, risk factors associated with cancer include, butare not limited to, (a) viral infection (e.g., herpes virus infection),(b) age, (c) family history, (d) heavy alcohol consumption, (e) obesity,and (f) tobacco use.

“An effective amount” as used herein refers to the amount of each activeagent required to confer therapeutic effect on the subject, either aloneor in combination with one or more other active agents. Effectiveamounts vary, as recognized by those skilled in the art, depending onthe particular condition being treated, the severity of the condition,the individual patient parameters including age, physical condition,size, gender and weight, the duration of the treatment, the nature ofconcurrent therapy (if any), the specific route of administration andlike factors within the knowledge and expertise of the healthpractitioner. These factors are well known to those of ordinary skill inthe art and can be addressed with no more than routine experimentation.It is generally preferred that a maximum dose of the individualcomponents or combinations thereof be used, that is, the highest safedose according to sound medical judgment. It will be understood by thoseof ordinary skill in the art, however, that a patient may insist upon alower dose or tolerable dose for medical reasons, psychological reasons,or for virtually any other reasons.

Empirical considerations, such as the half-life of a therapeuticcompound, generally contribute to the determination of the dosage. Forexample, antibodies that are compatible with the human immune system,such as humanized antibodies or fully human antibodies, may be used toprolong half-life of the antibody and to prevent the antibody beingattacked by the host's immune system. Frequency of administration may bedetermined and adjusted over the course of therapy, and is generally(but not necessarily) based on treatment, and/or suppression, and/oramelioration, and/or delay of a cancer. Alternatively, sustainedcontinuous release formulations of an anti-cancer therapeutic agent maybe appropriate. Various formulations and devices for achieving sustainedrelease are known in the art.

In some embodiments, dosages for an anti-cancer therapeutic agent asdescribed herein may be determined empirically in individuals who havebeen administered one or more doses of the anti-cancer therapeuticagent. Individuals may be administered incremental dosages of theanti-cancer therapeutic agent. To assess efficacy of an administeredanti-cancer therapeutic agent, one or more aspects of a cancer (e.g.,tumor formation, tumor growth, or cancer or tumor Type A-D) may beanalyzed.

Generally, for administration of any of the anti-cancer antibodiesdescribed herein, an initial candidate dosage may be about 2 mg/kg. Forthe purpose of the present disclosure, a typical daily dosage mightrange from about any of 0.1 μg/kg to 3 μg/kg to 30 μg/kg to 300 μg/kg to3 mg/kg, to 30 mg/kg to 100 mg/kg or more, depending on the factorsmentioned above. For repeated administrations over several days orlonger, depending on the condition, the treatment is sustained until adesired suppression or amelioration of symptoms occurs or untilsufficient therapeutic levels are achieved to alleviate a cancer, or oneor more symptoms thereof. An exemplary dosing regimen comprisesadministering an initial dose of about 2 mg/kg, followed by a weeklymaintenance dose of about 1 mg/kg of the antibody, or followed by amaintenance dose of about 1 mg/kg every other week. However, otherdosage regimens may be useful, depending on the pattern ofpharmacokinetic decay that the practitioner (e.g., a medical doctor)wishes to achieve. For example, dosing from one-four times a week iscontemplated. In some embodiments, dosing ranging from about 3 μg/mg toabout 2 mg/kg (such as about 3 μg/mg, about 10 μg/mg, about 30 μg/mg,about 100 μg/mg, about 300 μg/mg, about 1 mg/kg, and about 2 mg/kg) maybe used. In some embodiments, dosing frequency is once every week, every2 weeks, every 4 weeks, every 5 weeks, every 6 weeks, every 7 weeks,every 8 weeks, every 9 weeks, or every 10 weeks; or once every month,every 2 months, or every 3 months, or longer. The progress of thistherapy may be monitored by conventional techniques and assays and/or bymonitoring cancer Types A-D (1^(st)-4^(th) MF profile clusters,respectively) as described herein. The dosing regimen (including thetherapeutic used) may vary over time.

When the anti-cancer therapeutic agent is not an antibody, it may beadministered at the rate of about 0.1 to 300 mg/kg of the weight of thepatient divided into one to three doses, or as disclosed herein. In someembodiments, for an adult patient of normal weight, doses ranging fromabout 0.3 to 5.00 mg/kg may be administered. The particular dosageregimen, e.g., dose, timing, and/or repetition, will depend on theparticular subject and that individual's medical history, as well as theproperties of the individual agents (such as the half-life of the agent,and other considerations well known in the art).

For the purpose of the present disclosure, the appropriate dosage of ananti-cancer therapeutic agent will depend on the specific anti-cancertherapeutic agent(s) (or compositions thereof) employed, the type andseverity of cancer, whether the anti-cancer therapeutic agent isadministered for preventive or therapeutic purposes, previous therapy,the patient's clinical history and response to the anti-cancertherapeutic agent, and the discretion of the attending physician.Typically the clinician will administer an anti-cancer therapeuticagent, such as an antibody, until a dosage is reached that achieves thedesired result.

Administration of an anti-cancer therapeutic agent can be continuous orintermittent, depending, for example, upon the recipient's physiologicalcondition, whether the purpose of the administration is therapeutic orprophylactic, and other factors known to skilled practitioners. Theadministration of an anti-cancer therapeutic agent (e.g., an anti-cancerantibody) may be essentially continuous over a preselected period oftime or may be in a series of spaced dose, e.g., either before, during,or after developing cancer.

As used herein, the term “treating” refers to the application oradministration of a composition including one or more active agents to asubject, who has a cancer, a symptom of a cancer, or a predispositiontoward a cancer, with the purpose to cure, heal, alleviate, relieve,alter, remedy, ameliorate, improve, or affect the cancer or one or moresymptoms of the cancer, or the predisposition toward a cancer.

Alleviating a cancer includes delaying the development or progression ofthe disease, or reducing disease severity. Alleviating the disease doesnot necessarily require curative results. As used therein, “delaying”the development of a disease (e.g., a cancer) means to defer, hinder,slow, retard, stabilize, and/or postpone progression of the disease.This delay can be of varying lengths of time, depending on the historyof the disease and/or individuals being treated. A method that “delays”or alleviates the development of a disease, or delays the onset of thedisease, is a method that reduces probability of developing one or moresymptoms of the disease in a given time frame and/or reduces extent ofthe symptoms in a given time frame, when compared to not using themethod. Such comparisons are typically based on clinical studies, usinga number of subjects sufficient to give a statistically significantresult.

“Development” or “progression” of a disease means initial manifestationsand/or ensuing progression of the disease. Development of the diseasecan be detected and assessed using clinical techniques known in the art.Alternatively or in addition to the clinical techniques known in theart, development of the disease may be detectable and assessed based onthe cancer types (1^(st)-4^(th) MF profile types) described herein.However, development also refers to progression that may beundetectable. For purpose of this disclosure, development or progressionrefers to the biological course of the symptoms. “Development” includesoccurrence, recurrence, and onset. As used herein “onset” or“occurrence” of a cancer includes initial onset and/or recurrence.

In some embodiments, the anti-cancer therapeutic agent (e.g., anantibody) described herein is administered to a subject in need of thetreatment at an amount sufficient to reduce cancer (e.g., tumor) growthby at least 10% (e.g., 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% orgreater). In some embodiments, the anti-cancer therapeutic agent (e.g.,an antibody) described herein is administered to a subject in need ofthe treatment at an amount sufficient to reduce cancer cell number ortumor size by at least 10% (e.g., 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%or more). In other embodiments, the anti-cancer therapeutic agent isadministered in an amount effective in altering cancer type (e.g., fromcancer Type D to cancer Type A). Alternatively, the anti-cancertherapeutic agent is administered in an amount effective in reducingtumor formation or metastasis.

Conventional methods, known to those of ordinary skill in the art ofmedicine, may be used to administer the anti-cancer therapeutic agent tothe subject, depending upon the type of disease to be treated or thesite of the disease. The anti-cancer therapeutic agent can also beadministered via other conventional routes, e.g., administered orally,parenterally, by inhalation spray, topically, rectally, nasally,buccally, vaginally or via an implanted reservoir. The term “parenteral”as used herein includes subcutaneous, intracutaneous, intravenous,intramuscular, intraarticular, intraarterial, intrasynovial,intrasternal, intrathecal, intralesional, and intracranial injection orinfusion techniques. In addition, an anti-cancer therapeutic agent maybe administered to the subject via injectable depot routes ofadministration such as using 1-, 3-, or 6-month depot injectable orbiodegradable materials and methods.

Injectable compositions may contain various carriers such as vegetableoils, dimethylactamide, dimethyformamide, ethyl lactate, ethylcarbonate, isopropyl myristate, ethanol, and polyols (e.g., glycerol,propylene glycol, liquid polyethylene glycol, and the like). Forintravenous injection, water soluble anti-cancer therapeutic agents canbe administered by the drip method, whereby a pharmaceutical formulationcontaining the antibody and a physiologically acceptable excipients isinfused. Physiologically acceptable excipients may include, for example,5% dextrose, 0.9% saline, Ringer's solution, and/or other suitableexcipients. Intramuscular preparations, e.g., a sterile formulation of asuitable soluble salt form of the anti-cancer therapeutic agent, can bedissolved and administered in a pharmaceutical excipient such asWater-for-Injection, 0.9% saline, and/or 5% glucose solution.

In one embodiment, an anti-cancer therapeutic agent is administered viasite-specific or targeted local delivery techniques. Examples ofsite-specific or targeted local delivery techniques include variousimplantable depot sources of the agent or local delivery catheters, suchas infusion catheters, an indwelling catheter, or a needle catheter,synthetic grafts, adventitial wraps, shunts and stents or otherimplantable devices, site specific carriers, direct injection, or directapplication. See, e.g., PCT Publication No. WO 00/53211 and U.S. Pat.No. 5,981,568, the contents of each of which are incorporated byreference herein for this purpose.

Targeted delivery of therapeutic compositions containing an antisensepolynucleotide, expression vector, or subgenomic polynucleotides canalso be used. Receptor-mediated DNA delivery techniques are describedin, for example, Findeis et al., Trends Biotechnol. (1993) 11:202; Chiouet al., Gene Therapeutics: Methods And Applications Of Direct GeneTransfer (J. A. Wolff, ed.) (1994); Wu et al., J. Biol. Chem. (1988)263:621; Wu et al., J. Biol. Chem. (1994) 269:542; Zenke et al., Proc.Natl. Acad. Sci. USA (1990) 87:3655; Wu et al., J. Biol. Chem. (1991)266:338. The contents of each of the foregoing are incorporated byreference herein for this purpose.

Therapeutic compositions containing a polynucleotide may be administeredin a range of about 100 ng to about 200 mg of DNA for localadministration in a gene therapy protocol. In some embodiments,concentration ranges of about 500 ng to about 50 mg, about 1 μg to about2 mg, about 5 μg to about 500 μg, and about 20 μg to about 100 μg of DNAor more can also be used during a gene therapy protocol.

Therapeutic polynucleotides and polypeptides can be delivered using genedelivery vehicles. The gene delivery vehicle can be of viral ornon-viral origin (e.g., Jolly, Cancer Gene Therapy (1994) 1:51; Kimura,Human Gene Therapy (1994) 5:845; Connelly, Human Gene Therapy (1995)1:185; and Kaplitt, Nature Genetics (1994) 6:148). The contents of eachof the foregoing are incorporated by reference herein for this purpose.Expression of such coding sequences can be induced using endogenousmammalian or heterologous promoters and/or enhancers. Expression of thecoding sequence can be either constitutive or regulated.

Viral-based vectors for delivery of a desired polynucleotide andexpression in a desired cell are well known in the art. Exemplaryviral-based vehicles include, but are not limited to, recombinantretroviruses (see, e.g., PCT Publication Nos. WO 90/07936; WO 94/03622;WO 93/25698; WO 93/25234; WO 93/11230; WO 93/10218; WO 91/02805; U.S.Pat. Nos. 5,219,740 and 4,777,127; GB Patent No. 2,200,651; and EPPatent No. 0 345 242), alphavirus-based vectors (e.g., Sindbis virusvectors, Semliki forest virus (ATCC VR-67; ATCC VR-1247), Ross Rivervirus (ATCC VR-373; ATCC VR-1246) and Venezuelan equine encephalitisvirus (ATCC VR-923; ATCC VR-1250; ATCC VR 1249; ATCC VR-532)), andadeno-associated virus (AAV) vectors (see, e.g., PCT Publication Nos. WO94/12649, WO 93/03769; WO 93/19191; WO 94/28938; WO 95/11984 and WO95/00655). Administration of DNA linked to killed adenovirus asdescribed in Curiel, Hum. Gene Ther. (1992) 3:147 can also be employed.The contents of each of the foregoing are incorporated by referenceherein for this purpose.

Non-viral delivery vehicles and methods can also be employed, including,but not limited to, polycationic condensed DNA linked or unlinked tokilled adenovirus alone (see, e.g., Curiel, Hum. Gene Ther. (1992)3:147); ligand-linked DNA (see, e.g., Wu, J. Biol. Chem. (1989)264:16985); eukaryotic cell delivery vehicles cells (see, e.g., U.S.Pat. No. 5,814,482; PCT Publication Nos. WO 95/07994; WO 96/17072; WO95/30763; and WO 97/42338) and nucleic charge neutralization or fusionwith cell membranes. Naked DNA can also be employed. Exemplary naked DNAintroduction methods are described in PCT Publication No. WO 90/11092and U.S. Pat. No. 5,580,859. Liposomes that can act as gene deliveryvehicles are described in U.S. Pat. No. 5,422,120; PCT Publication Nos.WO 95/13796; WO 94/23697; WO 91/14445; and EP Patent No. 0524968.Additional approaches are described in Philip, Mol. Cell. Biol. (1994)14:2411, and in Woffendin, Proc. Natl. Acad. Sci. (1994) 91:1581. Thecontents of each of the foregoing are incorporated by reference hereinfor this purpose.

It is also apparent that an expression vector can be used to directexpression of any of the protein-based anti-cancer therapeutic agents(e.g., anti-cancer antibody). For example, peptide inhibitors that arecapable of blocking (from partial to complete blocking) a cancer causingbiological activity are known in the art.

In some embodiments, more than one anti-cancer therapeutic agent, suchas an antibody and a small molecule inhibitory compound, may beadministered to a subject in need of the treatment. The agents may be ofthe same type or different types from each other. At least one, at leasttwo, at least three, at least four, or at least five different agentsmay be co-administered. Generally anti-cancer agents for administrationhave complementary activities that do not adversely affect each other.Anti-cancer therapeutic agents may also be used in conjunction withother agents that serve to enhance and/or complement the effectivenessof the agents.

Treatment efficacy can be assessed by methods well-known in the art,e.g., monitoring tumor growth or formation in a patient subjected to thetreatment. Alternatively or in addition to, treatment efficacy can beassessed by monitoring tumor type over the course of treatment (e.g.,before, during, and after treatment). See, e.g., Example 5 below.

Combination Therapy

Compared to monotherapies, combinations of treatment approaches showedhigher efficacy in many studies, but the choice of remedies to becombined and designing the combination therapy regimen remainspeculative. Given that the number of possible combinations is nowextremely high, there is great need for a tool that would help to selectdrugs and combinations of remedies based on objective information abouta particular patient. Use of cancer MF profiles for designing orelecting a specific combination therapy establishes a scientific basisfor choosing the optimal combination of preparations.

When using MF profiles for designing a combination therapy one candefine a rational level of portrait detail. It is advisable to create aportrait of the modules with known therapeutic effectors, while modulesthat currently can't be influenced using medical approaches could beexcluded. At the same time, there may be modules that are important tothe outcome of the disease, having no effectors embodied in drugs orother therapies (e.g., radiation, cell therapy, oncolytic viruses,etc.). Such modules may have scientific value and their preservation isreasonable in portraits intended for research work.

As noted above, also provided herein are methods of treating a cancer orrecommending treating a cancer using any combination of anti-cancertherapeutic agents or one or more anti-cancer therapeutic agents and oneor more additional therapies (e.g., surgery and/or radiotherapy). Theterm combination therapy, as used herein, embraces administration ofmore than one treatment (e.g., an antibody and a small molecule or anantibody and radiotherapy) in a sequential manner, that is, wherein eachtherapeutic agent is administered at a different time, as well asadministration of these therapeutic agents, or at least two of theagents or therapies, in a substantially simultaneous manner.

Sequential or substantially simultaneous administration of each agent ortherapy can be affected by any appropriate route including, but notlimited to, oral routes, intravenous routes, intramuscular, subcutaneousroutes, and direct absorption through mucous membrane tissues. Theagents or therapies can be administered by the same route or bydifferent routes. For example, a first agent (e.g., a small molecule)can be administered orally, and a second agent (e.g., an antibody) canbe administered intravenously.

As used herein, the term “sequential” means, unless otherwise specified,characterized by a regular sequence or order, e.g., if a dosage regimenincludes the administration of an antibody and a small molecule, asequential dosage regimen could include administration of the antibodybefore, simultaneously, substantially simultaneously, or afteradministration of the small molecule, but both agents will beadministered in a regular sequence or order. The term “separate” means,unless otherwise specified, to keep apart one from the other. The term“simultaneously” means, unless otherwise specified, happening or done atthe same time, i.e., the agents of the invention are administered at thesame time. The term “substantially simultaneously” means that the agentsare administered within minutes of each other (e.g., within 10 minutesof each other) and intends to embrace joint administration as well asconsecutive administration, but if the administration is consecutive itis separated in time for only a short period (e.g., the time it wouldtake a medical practitioner to administer two agents separately). Asused herein, concurrent administration and substantially simultaneousadministration are used interchangeably. Sequential administrationrefers to temporally separated administration of the agents or therapiesdescribed herein.

Combination therapy can also embrace the administration of theanti-cancer therapeutic agent (e.g., an antibody) in further combinationwith other biologically active ingredients (e.g., a vitamin) andnon-drug therapies (e.g., surgery or radiotherapy).

It should be appreciated that any combination of anti-cancer therapeuticagents may be used in any sequence for treating a cancer. Thecombinations described herein may be selected on the basis of a numberof factors, which include but are not limited to the effectiveness ofaltering identified tumor type (e.g., Type A-D), reducing tumorformation or tumor growth, and/or alleviating at least one symptomassociated with the cancer, or the effectiveness for mitigating the sideeffects of another agent of the combination. For example, a combinedtherapy as provided herein may reduce any of the side effects associatedwith each individual members of the combination, for example, a sideeffect associated with an administered anti-cancer agent.

In some embodiments, an anti-cancer therapeutic agent is an antibody, animmunotherapy, a radiation therapy, a surgical therapy, and/or achemotherapy.

Examples of the antibody anti-cancer agents include, but are not limitedto, alemtuzumab (Campath), trastuzumab (Herceptin), Ibritumomab tiuxetan(Zevalin), Brentuximab vedotin (Adcetris), Ado-trastuzumab emtansine(Kadcyla), blinatumomab (Blincyto), Bevacizumab (Avastin), Cetuximab(Erbitux), ipilimumab (Yervoy), nivolumab (Opdivo), pembrolizumab(Keytruda), atezolizumab (Tecentriq), avelumab (Bavencio), durvalumab(Imfinzi), and panitumumab (Vectibix).

Examples of an immunotherapy include, but are not limited to, a PD-1inhibitor or a PD-L1 inhibitor, a CTLA-4 inhibitor, adoptive celltransfer, therapeutic cancer vaccines, oncolytic virus therapy, T-celltherapy, and immune checkpoint inhibitors.

Examples of radiation therapy include, but are not limited to, ionizingradiation, gamma-radiation, neutron beam radiotherapy, electron beamradiotherapy, proton therapy, brachytherapy, systemic radioactiveisotopes, and radiosensitizers.

Examples of a surgical therapy include, but are not limited to, acurative surgery (e.g., tumor removal surgery), a preventive surgery, alaparoscopic surgery, and a laser surgery.

Examples of the chemotherapeutic agents include, but are not limited to,Carboplatin or Cisplatin, Docetaxel, Gemcitabine, Nab-Paclitaxel,Paclitaxel, Pemetrexed, and Vinorelbine.

Additional examples of chemotherapy include, but are not limited to,Platinating agents, such as Carboplatin, Oxaliplatin, Cisplatin,Nedaplatin, Satraplatin, Lobaplatin, Triplatin, Tetranitrate,Picoplatin, Prolindac, Aroplatin and other derivatives; Topoisomerase Iinhibitors, such as Camptothecin, Topotecan, irinotecan/SN38, rubitecan,Belotecan, and other derivatives; Topoisomerase II inhibitors, such asEtoposide (VP-16), Daunorubicin, a doxorubicin agent (e.g., doxorubicin,doxorubicin hydrochloride, doxorubicin analogs, or doxorubicin and saltsor analogs thereof in liposomes), Mitoxantrone, Aclarubicin, Epirubicin,Idarubicin, Amrubicin, Amsacrine, Pirarubicin, Valrubicin, Zorubicin,Teniposide and other derivatives; Antimetabolites, such as Folic family(Methotrexate, Pemetrexed, Raltitrexed, Aminopterin, and relatives orderivatives thereof); Purine antagonists (Thioguanine, Fludarabine,Cladribine, 6-Mercaptopurine, Pentostatin, clofarabine, and relatives orderivatives thereof) and Pyrimidine antagonists (Cytarabine,Floxuridine, Azacitidine, Tegafur, Carmofur, Capacitabine, Gemcitabine,hydroxyurea, 5-Fluorouracil (5FU), and relatives or derivativesthereof); Alkylating agents, such as Nitrogen mustards (e.g.,Cyclophosphamide, Melphalan, Chlorambucil, mechlorethamine, Ifosfamide,mechlorethamine, Trofosfamide, Prednimustine, Bendamustine, Uramustine,Estramustine, and relatives or derivatives thereof); nitrosoureas (e.g.,Carmustine, Lomustine, Semustine, Fotemustine, Nimustine, Ranimustine,Streptozocin, and relatives or derivatives thereof); Triazenes (e.g.,Dacarbazine, Altretamine, Temozolomide, and relatives or derivativesthereof); Alkyl sulphonates (e.g., Busulfan, Mannosulfan, Treosulfan,and relatives or derivatives thereof); Procarbazine; Mitobronitol, andAziridines (e.g., Carboquone, Triaziquone, ThioTEPA,triethylenemalamine, and relatives or derivatives thereof); Antibiotics,such as Hydroxyurea, Anthracyclines (e.g., doxorubicin agent,daunorubicin, epirubicin and relatives or derivatives thereof);Anthracenediones (e.g., Mitoxantrone and relatives or derivativesthereof); Streptomyces family antibiotics (e.g., Bleomycin, Mitomycin C,Actinomycin, and Plicamycin); and ultraviolet light.

EXAMPLES

In order that the invention described herein may be more fullyunderstood, the following examples are set forth. The examples describedin this application are offered to illustrate the methods, compositions,and systems provided herein and are not to be construed in any way aslimiting their scope.

Example 1: Methods

Molecular and Clinical Data

Genomic, transcriptomic and clinical data for 23 solid tumors from TheCancer Genome Atlas (TCGA) were downloaded via the TCGA data portal(tcga-data.nci.nih.gov). Mutations were obtained out of correspondingTCGA MAF files. RNA-sequencing data were downloaded and processed inFPKM units. Tumor samples were used.

Creating Biologically Relevant Gene Sets to Evaluate Processes in aTumor Microenvironment

To visualize the composition of a patient's tumor microenvironment andthe immune system processes occurring within the tumor, an approachbased on analysis of signature gene lists was used. The analysisrequired associating target gene expression with biological processesand/or cell functions. The signatures used in the analysis comprised adiverse set of adaptive and innate immune cell types, as well as tumortissue functioning and growth associated processes. The latter includedtumor-supporting components of microenvironment: cancer-associatedfibroblasts, tumor vasculature abundance and angiogenesis-inducingprocesses. Tumor purity (cellularity) indicating percentage of malignantcells in a tumor was also included in the visualization of the patient'scancer-immune portrait to represent size of the malignant compartment.The immune-related gene expression signatures comprised 327 genes. Geneshighly specific to the functional process they describe were selected.

The list of gene set annotations is shown in Table 2. The created genesets were compared to The Molecular Signatures Database (MSigDB), apublicly available collection of annotated gene sets. The similaritybetween proposed gene sets and the MSigDB collection was calculatedusing a hypergeometric test (FDR <0.05). Each gene was scientificallyvalidated to represent its true influence on the process for which itwas designated. Gene annotations were confirmed using scientificpublications.

TABLE 2 List of Gene Set Annotations. Level 1 Level 2 Level 3 GMTAnti-tumor Antigen MHCI HLA-A HLA-C TAP1 immune infiltrate presentationHLA-B B2M TAP2 HLA-DRA HLA-DOA HLA-DQA1 HLA-DRB1 HLA-DPA1 HLA-DRB5 MHCIIHLA-DOB HLA-DPB1 HLA-DQA2 HLA-DPB2 HLA-DMB HLA-DQB2 HLA-DMA HLA-DQB1HLA-DRB6 Coactivation CD80 TNFRSF4 CD83 molecules CD40 CD86 COSLG CD28IFNG LCK FASLG PRF1 GNLY TBX21 Cytotoxic T and Effector cells ZAP70 GZMBCD8A NK cells GZMA GZMK CD8B EOMES NKG7 KIR2DS1 GNLY KLRK1 CD244 KIR2DS3KIR2DL4 NK cells CD160 GZMH KLRC2 CD226 KIR2DS2 IFNG KIR2DS5 NCR1KIR2DS4 CXCL9 CCR7 CCL3 T cell traffic CXCL10 CXCL11 CCL4 CXCR3 CCL21CCL5 CX3CL1 CCL2 EOMES CD3G UBASH3A T cells CD3E LCK CD3D TRBC2 ITKTRBC1 TBX21 TRAC TRAT1 CD19 CR2 CD79B B cells B cells CD24 CD79A CD27CD22 TNFRSF13C NFRSF13B MS4A1 TNFRSF17 BLK IL23A Anti-tumor NOS2 TNFIL1B microenvironment M1 signatures IL12B IL12A SOCS3 IFNG CD27 IL15 Th1signature CD4OLG IL2 TBX21 LTA HMGB1 TNF NFSF10 Antitumor cytokinesIFNB1 IFNA2 FASLG CCL3 PDCD1 CD274 Tumor-promoting Checkpoint CheckpointCTLA4 LAG3 HAVCR2 immune infiltrate inhibition inhibition PDCD1LG2 BTLAVSIR CXCL12 IL10 CCL1 TGFB1 TNFRSF1B CCL2 TGFB2 CCL17 CCL5 Treg TregTGFB3 CXCR4 CXCL13 FOXP3 CCR4 CCL28 CTLA4 CCL22 IDO1 NOS2 CCL4 ARG1 CYBBCCL8 IL4R CXCR4 CCR2 MDSC MDSC IL10 CD33 CCL3 TGFB1 CXCL1 CCL5 TGFB2CXCL5 CSF1 TGFB3 CCL2 CXCL8 CXCL8 PRG2 MS4A2 CXCL2 EPX CPA3 CXCL1 RNASE2IL4 Granulocytes Granulocytes CCL11 RNASE3 IL5 CCL24 IL5RA IL13 KITLGGATA1 SIGLEC8 CCL5 SIGLEC8 MPO CXCL5 PRG3 ELANE CCR3 CMA1 PRTN3 CCL26TPSAB1 CTSG IL10 MRC1 MSR1 Tumor- M2 signature VEGFA CSF1 CD163promoting TGFB1 LRP1 CSF1R immune infiltrate IDO1 ARG1 PTGS1 PTGES Th2signature IL4 IL13 IL25 IL5 IL10 GATA3 Protumor cytokines IL10 TGFB 2IL22 TGFB1 TGFB3 MIF Complement CFD CD55 CR1 inhibition CFI CD46 LGALS1TGFB1 FAP COL1A1 TGFB2 LRP1 COL1A2 TGFB3 CD248 Fibroblasts CAF CAFCOL4A1 ACTA2 COL6A1 COL5A1 PGF2 COL6A2 COL6A3 VEGFA KDR MMRN1 VEGFBANGPT1 LDHA VEGFC ANGPT2 HIFI A PDGFC TEK EPAS1 AngiogenesisAngiogenesis Angiogenesis CXCL8 VWF CA9 CXCR2 CDH5 SPP1 FLT1 NOS3 LOXPIGF KDR SLC2A1 CXCL5 VCAM1 LAMP3 MKI67 AURKA MYBL2 ESCO2 AURKB BUB1CETN3 CDK4 PLK1 Tumor Properties Proliferation rate Proliferation rateCDK2 CDK6 CCNB1 CCND1 PRC1 MCM2 CCNE1 E2F1 MCM6 Activated PI3K/AKT/mTORPIK3CA AKT1 PRKCA signaling signaling PIK3CB MTOR AKT2 pathways PIK3CGPTEN AKT3 PIK3CD RAS/RAF/MEK BRAF MAP2K1 MKNK1 signaling FNTA MAP2K2MKNK2 FNTB ALK MET ERBB4 AXL Receptor tyrosine KIT NTRK1 ERBB3 kinasesexpression EGFR FGFR1 BCR-ABL ERBB2 FGFR2 PDGFRA FGFR3 PDGFRB FLT3Growth Factors NGF FGF7 IL7 CSF3 IGF1 FGF2 CSF2 IGF2 TP53 Tumor Tumorsuppressors SIK1 DCN AIM2 suppressors PTEN MTAP RB1 ESRP1 SMARCA4 NEDD9Metastasis Metastasis signature CTSL SNAI2 PAPPA signature HOXA1 TWIST1HPSE KISS 1 TCF21 Antimetastatic Antimetastatic ADGRG1 CDH1 NCAM1factors factors BRMS1 PCDH10 MITF Mutation status Mutation status MajorRecurrent Mutations Additional modules Malignant cells PurityNon-malignant microenvironment 1-PurityQuantification of Process Intensity

ssGSEA enrichment scores (ES) were calculated using the GSVA R packagewith default parameters (gsea method with type=“ssgsea”;normalized=True). ES were then transformed into z-scores and clipped tothe range [−4, 4] for each functional process in each dataset.

For tumor purity estimation, CPE metric values obtained from Aran et al.Systematic pan-cancer analysis of tumour purity. Nat Commun. NaturePublishing Group; 2015; 6:8971 were used. Tumor infiltration cell number(nonmalignant cell number) was calculated as 1—tumor purity.

Mutation data including presence of driver mutations and total number ofnonsynonymous mutations for the “Mutation status” node was obtained fromTCGA MAF files.

Quantification of Tumor Microenvironment with Deconvolution Methods

Cell type deconvolution was performed using CIBERSORT with LM22 matrixand MCP-counter, capable of estimating the abundance oftissue-infiltrating immune and stromal cell populations according togene expression. In addition, single-sample GSEA (ssGSEA), an extensionof Gene Set Enrichment Analysis (GSEA), was performed on widely usedgene signatures of immune infiltrate.

Hierarchical Organization of Processes

Biological properties describing the tumor microenvironment and tumorprocesses were hierarchically organized according to their associatedbiology. A clustered graph structure was created from descriptions fromthe highest to the lowest granularity including genes, biologicalprocesses including high-level and low-level processes, and biologicalcategories. The high-level processes were chosen as follows: tumor (astumor burden or tumor purity), tumor nonmalignant microenvironmentcomprised of the angiogenesis module, cancer-associated fibroblasts,tumor-promoting, and anti-tumor immune infiltrates. As a non-limitingexample, CD80 genes were made part of a “co-activation molecules”process, which was a part of an “antigen presentation” process, which inturn was part of an “anti-tumor immune infiltration” module. Geneannotations for each high- or low-level process are presented in Table2. Using the determined hierarchical organization of the tumorprocesses, a cancer-immune portrait was visualized at different levelsof detail.

Visualization of the Cancer-Immune Portrait

Portraits were visualized as a graph based structure using Mathematica11 standard packages (Wolfram Research, USA). A node size that describedan intensity of a process in a particular patient was taken according toa normalized score calculated for process intensity. A distribution ofssGSEA enrichment scores for each process was mapped to the range of (0,1), by a cumulative distribution function (CDF) within the correspondingTCGA cohort. Driver mutations influencing therapeutic and prognosticoutcomes were depicted in the tumor properties group as the “Mutationstatus” node representing a total number of nonsynonymous mutationsfound in the patient tumor, while the upper genes arising from this nodedemonstrated recurrent mutations. The “mutation status” node size wasalso transformed to the range of (0, 1) by CDF from the correspondingcohort distribution.

All the processes were labeled either anti-tumor or pro-tumor.Anti-tumor processes were colored in a blue gradient, pro-tumorprocesses were colored in a burgundy gradient. The intensity (i.e.,intensity of the shade or darkness/lightness) represented processintensity. Gene nodes were accorded a fixed size and color using thesame method as the processes. The size of the “Malignant cells”, as wellas the “Non-malignant microenvironment” nodes were visualized based onthe tumor purity. The same visualization principles were applied to themolecular functional portraits with different levels of detail.

Survival Analysis

Survival curves were calculated according to the Kaplan-Meier method,and differences between curves were assessed using the log-rank test.

Dense Clustering

Edges that represent <40% samples correlation were removed to getconnected graphs with <1% node connectivity (˜0.6% for pan-cancer andSKCM). Node connectivity was calculated using the NetworkX pythonpackage. All edges with weight >50 were removed, leading to graphconnectivity break.

The similarity of tumor samples was measured using the Pearsoncorrelation [−1, 1] between process intensities (ssGSEA enrichmentscores). Similarities in the space of 28 processes were calculated usingpython pandas and SciPy. Distance matrix was converted into a NetworkXgraph as follows: each sample formed a node; two nodes formed an edgewith weight equal to their Pearson correlation. Later edges with weight<0.4 were removed. The Louvain community detection algorithm was appliedto calculate graph partitioning into clusters using python-louvain withdefault parameters. Final partitions were labeled as Types A-D(1^(st)-4^(th) MF profile clusters, respectively).

Dense clusters were visualized in Cytoscape (v3.4.0). Nodes wereorganized using “Perfuse force directed layout” (default springcoefficient=1e-5, number of iterations=100). Node size represents thenumber of its neighbors (adjacent edges). Node color corresponds totumor subtype (A-D) unless otherwise specified.

K-Means Clustering

28 functional processes were organized into four clusters using GENE-Ek-means algorithm with 20,000 iterations using the Pearson correlationas a distance metric.

Comparison of Clusters by the Process Values

Comparison of each process activity between cluster pairs was performedby t-test. Per-cluster prevalence and deficiency of mutations in thedriver genes were analyzed by the Fisher exact test.

Heatmaps

Python-matplotlib (v1.5.1) or python-seaborn (v0.7.1) or GENE-E wereused to create heatmaps. The Pearson correlation was used as the defaultsimilarity metric (unless otherwise mentioned) for correlation matrixes.Hierarchical clustering was performed using complete linkage andeuclidean distance for correlation matrixes clustering.

tSNE

tSNE analysis was performed by Rtsne (v0.13) package in R and visualizedby R plot function.

Validating Prevalent (Dominant) Molecular-Functional Types of Cancers

In order to validate the proposed molecular-functional types of cancerorganization, additional analysis was performed. The additional analysisshowed the dominant clusters of MF profiles in 20 epithelial cancers.The additional analysis further showed that the quantified melanomamicroenvironment activity by functional process scores formed explicitclusters, but that the underlying expressions of the 10,000 mostexpressed genes did not, due to the increased noise generated from theaddition of multiple unnecessary and unrelated genes. In that sense,expression profiles of 298 genes composing functional processes showed afuzzier structure with less distinct clusters than expression profilesof functional process scores. Pan-cancer patient correlation analysisalso confirmed the formation of distinct types of cancermolecular-functional portraits.

Prevalent Types Analysis

MCP-counter, CIBERSORT, and cell deconvolution algorithms were appliedto the RNA-Seq data of 470 melanoma patients. This analysis demonstratedthat Type A, B, C and D clusters (1^(st)-4^(th) MF profile clusters,respectively) were segregated using the MCP-counter but not withCIBERSORT. Notably, the MCP-counter revealed the main types ofleukocytes and lymphocytes, tumor-associated fibroblasts, andendothelial cells.

CIBERSORT with LM22 matrix provided a composition ofleukocyte/lymphocyte infiltrate, but did not take into accountendothelial cells and CAFs. However, Types A and B (first and second MFprofile clusters, respectively) melanomas displaying dominant CD8 Tcells were segregated from Types C and D (third and fourth MF profileclusters, respectively) displaying dominant tumor-associated macrophages(e.g., M2 macrophages) using CIBERSORT/LM22.

Deconvolution methods based on gene sets proposed by Senbabaoglu et al.were analyzed. See Senbabaoglu et al. Tumor immune microenvironmentcharacterization in clear cell renal cell carcinoma identifiesprognostic and immunotherapeutically relevant messenger RNA signatures;Genome Biology (2016) 17:231, which is herein incorporated by referencefor this purpose. However, this analysis was unable to differentiatebetween the four tumor cell clusters corresponding to Types A-D(1^(st)-4^(th) MF profile clusters, respectively).

Driver mutations are thought to be key factors of tumorigenesis. Inorder to analyze whether driver mutations are associated with any of theprevalent four types of melanomas, the abundance of such mutations ineach cluster along with their enrichment or deficiency in the clusterwas computed. Fisher's exact test was used to evaluate the cluster'senrichment with samples containing mutations in any given gene. However,after correction for multiple testing, no driver genes showedsignificant (FDR<0.05) enrichment. The APC gene (FDR=0.084) had almostreached a significant cutoff as being overrepresented (incidence ratio2.38) in Type D melanomas.

Results for 38 driver mutations found in different melanoma types areprovided in Table 3. Values that were statistically significant insingle tests (p-value <0.05) are marked. Mutation-rich melanoma types(underlined) had a relative abundance over 1.0, and mutation-deficientmelanoma types (bold) had a relative abundance under 1.0.

TABLE 3 Percent of patients with indicated mutations and relativeabundance of these mutations in the melanoma cohort (470 patients).Patients with indicated mutation in the given Relative abundance ofmutation compared to melanoma type the whole melanoma cohort A B C D A BC D APC 5.1% 8.6% 2.9% 17.7% 0.68 1.16 0.39 2.38 ARID1A 2.2% 5.2% 5.8%3.8% 0.51 1.22 1.37 0.89 ATM 3.6% 5.2% 5.8% 3.8% 0.78 1.11 1.24 0.81ATRX 4.3% 6.0% 7.2% 2.5% 0.82 1.14 1.37 0.48 BAP1 0.7% 2.6% 0.7% 2.5%0.49 1.74 0.49 1.70 BRAF 50.7% 50.0% 57.2% 32.9% 1.03 1.01 1.16 0.67BRCA2 5.1% 4.3% 8.0% 7.6% 0.82 0.70 1.29 1.23 CDH1 0.0% 1.7% 2.2% 2.5%0.00 1.16 1.46 1.70 CDKN2A 6.5% 12.1% 7.2% 11.4% 0.73 1.35 0.81 1.28CTCF 0.0% 0.0% 1.4% 1.3% 0.00 0.00 2.28 1.99 CTNNB1 4.3% 5.2% 4.3% 5.1%0.93 1.11 0.93 1.08 DNMT3 2.2% 3.4% 1.4% 3.8% 0.85 1.35 0.57 1.49 EGFR7.2% 1.7% 3.6% 13.9% 1.22 0.29 0.61 2.34 FBXW7 1.4% 1.7% 4.3% 5.1% 0.490.58 1.46 1.70 FLT3 4.3% 12.9% 8.0% 11.4% 0.50 1.49 0.92 1.31 GATA3 1.4%2.6% 0.0% 6.3% 0.68 1.22 0.00 2.98 HRAS 0.7% 1.7% 1.4% 0.0% 0.68 1.621.37 0.00 IDH1 3.6% 6.9% 2.2% 6.3% 0.81 1.55 0.49 1.42 KRAS 1.4% 2.6%2.2% 2.5% 0.68 1.22 1.02 1.19 MAP3K1 1.4% 0.9% 0.7% 0.0% 1.71 1.02 0.850.00 MTOR 3.6% 6.9% 5.1% 7.6% 0.66 1.25 0.92 1.38 NAV3 5.1% 12.9% 12.3%11.4% 0.50 1.27 1.21 1.12 NCOR1 0.7% 4.3% 7.2% 12.7% 0.13 0.78 1.31 2.29NF1 7.2% 13.8% 8.7% 20.3% 0.63 1.20 0.76 1.77 NOTCH1 2.2% 2.6% 2.2% 5.1%0.79 0.94 0.79 1.83 NPM1 0.0% 0.9% 1.4% 1.3% 0.00 1.02 1.71 1.49 NRAS18.1% 30.2% 23.9% 41.8% 0.68 1.13 0.89 1.56 PBRM1 3.6% 4.3% 4.3% 6.3%0.81 0.97 0.98 1.42 PIK3CA 2.2% 1.7% 2.2% 2.5% 1.02 0.81 1.02 1.19PIK3R1 2.2% 0.9% 1.4% 0.0% 1.71 0.68 1.14 0.00 PTEN 2.9% 6.9% 8.7% 1.3%0.55 1.30 1.64 0.24 RB1 2.9% 2.6% 1.4% 1.3% 1.37 1.22 0.68 0.60 RUNX11.4% 0.0% 0.0% 0.0% 3.41 0.00 0.00 0.00 SETD2 1.4% 9.5% 3.6% 7.6% 0.281.86 0.71 1.49 STAG2 0.7% 3.4% 2.2% 0.0% 0.43 2.03 1.28 0.00 TAF1 0.7%6.0% 2.2% 3.8% 0.24 2.03 0.73 1.28 TP53 10.1% 14.7% 7.2% 17.7% 0.87 1.260.62 1.52 VHL 0.0% 0.0% 0.7% 1.3% 0.00 0.00 1.71 2.98

Prevalent melanoma types appeared to be enriched or deficient indistinct sets of driver mutations. It appeared that key MAPK pathwaygenes varied according to the mutation rate among four melanoma types.In addition, no single factor explicitly defined a melanoma type to aspecific mutation. Taken together, these results suggested thatmutations are associated with but do not determine themolecular-functional types of these cancers (i.e., cancer Types A-D;1^(st)-4^(th) MF profile clusters, respectively).

Classification of Tumor Organization into Four Prevalent Types

The following procedure was used for preprocessing data from TCGA:

-   -   1) Calculated TCGA X cancer cohort processes values using        ssGSEA. Calculated mean and standard of each process. Obtained        Z-score TCGA X cancer cohort.

${ZscoredSampleProcess}_{x} = \frac{{SampleProcess}_{x} - {{mean}\left( {TCGACohortProcess}_{x} \right)}}{{std}\left( {TCGACohortProcess}_{x} \right)}$

-   -   2) Calculated patient's processes values using ssGSEA. Obtained        Z-score patients processes values using mean and standard from        previous step cancer cohort.

${ZscoredPatentProcess}_{x} = \frac{{PatentsProcess}_{x} - {{mean}\left( {TCGACohortProcess}_{x} \right)}}{{std}\left( {TCGACohortProcess}_{x} \right)}$

-   -   3) Patient's sample was classified according to MF profile type        with the closest (smallest) distance from patient's processes        z-scored vector to MFP cancer cohort centroids.    -   4) Distance was calculated as Euclidean distance in z-scored        processes space or (1-pearson/spearman correlation).    -   5) 1-distance to the each of MF profile types was treated as a        similarity measure in the case of intermediate cases (if, for        example, a patient's sample was very close to 2 prevalent types        resulting in mixed features from both types).

The following procedure was used for preprocessing other data types(e.g., data from DNA microarrays, other references, outlying patientdata from TCGA X cancer cohort PCA projection into 2-dimm space):

-   -   1) Calculated TCGA X cancer cohort processes values using        ssGSEA, Z-score TCGA X cancer cohort.    -   2) Obtained a cohortA of patients with X cancer, processed them        similarly as the patient (>40 samples). Calculated cohortA        processes values using ssGSEA.    -   3) Calculated patient's processes values using ssGSEA. Obtained        Z-score for combined cohortA and the patient.    -   4) If z-scored TCGA X cancer cohort and z-scored cohortA admixed        on combined PCA 2-dimm projection, the procedure continued with        step 3 described herein.

Example 2: Creating a Molecular Functional (MF) Portrait of a Tumor

A bioinformatics pipeline was constructed to determine tumor properties(e.g., malignant properties, non-malignant properties), and depict thetumor properties in a Molecular Functional Portrait (MF profile). The MFprofile was designed to depict tumor cell composition and functionalactivities, and to facilitate the practical use of such information incancer therapy. An exemplary bioinformatics pipeline for constructing atumor portrait is shown in FIG. 1A. An exemplary MF profile is shown inFIG. 1B.

In brief, the bioinformatics pipeline was used to (i) evaluate theintrinsic properties of tumor cells such as oncogenic pathways,proliferation rate, epithelial-mesenchymal transition (EMT), andmetastatic capacities; (ii) reconstruct the comprehensive immune,stromal and vascular networks of the tumor microenvironment; (iii)quantify the functional activities of different tumor associated celltypes; and (iv) determine the intensity of processes that collectivelyeither stimulated or inhibited progressive tumor growth.

Tumor cell composition was reconstructed from RNA-Seq and Exome-Seq dataof tumor and normal tissue using in silico methods for inferring tumorpurity and through deconvolution of the expression profiles forassessing functional subsets of both infiltrating hematopoietic cellsand stromal cells. RNA-Seq data also provided a measure of certaincellular processes based on the expression of specific gene signaturesassociated with defined biological functions distributed among differentcell types, such as antigen presentation, metastasis and inflammation.

A comprehensive cancer model was formulated by analysis of more than 373publications, and yielded 28 functional modules listed in Table 2. Theintensity of the “Mutation status” module was evaluated throughquantitating mutations in 38 driver genes. The intensities of theremaining 27 modules were evaluated by gene set enrichment analysis(ssGSEA) on custom built signatures, which enabled estimation of theactivity of different intratumoral processes. Taken together, thesemodules inherently reflected the relative content of the main cell typesin a tumor tissue.

The qualitative and quantitative functional properties as theintensities of processes in 28 functional modules were graphicallydepicted in FIG. 1B. Module size corresponds to its ssGSEA enrichmentscore (or mutation counts) normalized within the same TCGA cohort.Colors reflect the module pro- or anti-cancer activity. Solid shadeswithout cross-marking were assigned to the modules that promote tumorgrowth, while shades shades with cross-marking were assigned to thosehaving anti-cancer activity. The coloration of the modules was alsodependent on the ssGSEA score.

Example 3: Prevalent Types of Melanoma According to theirStructural-Functional Organization Revealed Via an MF Profile

The visualization method described herein enables a user to study thestructural and functional composition of a particular patient's tumor,as well as to compare tumors from different patients. MF profiles for470 patient human skin cutaneous melanoma (SKCM) tumors were constructedusing data available from TCGA. The MF profile of each particularpatient tumor was unique, yet the model clearly revealed a similarity oftumor MF profiles among different patients (FIG. 41A).

The prevalent types of melanoma tumors were further revealed usingunsupervised dense clustering analysis based on detection of the tightlyconnected networks of similar patients within the patients' correlationgraph (FIG. 41B). This analysis revealed that the graph contained fourdistinct dense subpopulations (FIG. 41C). These four tumor types werelabeled as Types A, B, C and D (1^(st)-4^(th) MF profile clusters,respectively). Analysis of tumor type abundance demonstrated that TypeA, B, C, and D tumors were present in 22%, 28%, 24%, and 24% of melanomapatients, respectively. In other words, 98% of melanoma patients couldbe determined to have one of the four prevalent tumor types.

As an alternative approach, k-means clustering was applied, which gaverise to nearly the same clusters of patients as the unsupervised denseclustering approach (FIG. 41D). These clusters were also supported bytumor dissection using MCP-counter cell (FIG. 41E), CIBERSORTdeconvolution algorithm (FIG. 41E), and by dissection based onphenotype-specific gene signatures (FIG. 41F).

These four types of MF profiles are significantly different according tothe activity of 28 functional modules. Inter-cluster analysis revealedthat the differences between the clusters resides in the activity oftheir underlying processes (FIG. 41G). Process activity between thecluster pairs were compared using the t-test. Each pair of clustersdiffered by the activity of at least six processes with a p-value <10⁻⁷(FIG. 41H).

The four tumor types were characterized in terms of patient prognosisand abundance of driver mutations. Patients having Types A and B (firstand second MF profile clusters, respectively) melanomas hadsignificantly longer survival time as compared to patients with Types Cand D melanomas (third and fourth MF profile clusters, respectively)(FIGS. 41I and 41J). The four prevalent melanoma types appeared to beenriched or deficient in distinct sets of driver mutations, yet it wasrevealed that mutations are associated but do not determinemolecular-functional types of melanoma tumors (FIG. 41K).

Detailed MF profiles representative for Types A-D melanomas(1^(st)-4^(th) MF profile clusters, respectively) are shown in FIGS.42A-42D. Types A and B (first and second MF profile clusters,respectively) were characterized as “inflamed” tumors, and types C and D(third and fourth MF profile clusters, respectively) were characterizedas “noninflamed” tumors. “Inflamed” tumors are characterized byexcessive infiltration with immune cells. “Noninflamed” tumors arepoorly infiltrated by hematopoietic cells.

Human skin cutaneous melanomas characterized as MF profile type A werecharacterized by abundant infiltration of immune cells and the presenceof factors necessary for antigen presentation to T cells and theiractivation (e.g., MHC class I and II, CD80, CD86, CD40, etc.). Anaverage ratio of malignant to nonmalignant cells (tumor purity) in thistype of melanoma was 0.57. Type A cancers have pronounced signs of tumorinfiltration by immune cells known to possess anticancer effectoractivity (e.g., cytotoxic T, NK cells, Th1 and M1 cells). Balancedagainst anti-cancer (e.g., anti-tumor) processes, Type A tumors alsodemonstrated active expression of checkpoint inhibitor molecules andrecruitment of suppressor cells (e.g., MDSC and Treg), as well as othertypes of cells that support tumor growth (e.g., M2 and Th2). Type Atumors had a highly developed network of blood vessels and an increasedconcentration of cancer-associated fibroblasts, which promoteepithelial-mesenchymal transition and malignant cell metastatic spread.Taken together, the analysis revealed that Type A tumors arecharacterized by high intensities of both anticancer and pro-cancerimmune processes.

Type B melanoma tumors had similar features to Type A melanoma tumorsexcept that Type B tumors demonstrated a lower intensity of tumorimmune/inflammatory infiltration and lacked extensive angiogenesis andCAF networks. Type B melanoma tumors had 0.64 tumor purity on average.

Type C melanoma tumors and Type D melanoma tumors were demonstrated tohave poor or no leukocyte/lymphocyte infiltration. Type C melanomatumors had extensive vascularization and increased levels of CAFs. Bycontrast, excessive angiogenesis and CAF networks were not found in TypeD melanoma tumors. Average tumor purities for Type C melanoma tumors andType D melanoma tumors were 0.81 and 0.85, respectively, reflecting thepredominance of malignant cells.

Type B melanoma tumors and Type D melanoma tumors were characterized byhigh tumor proliferation rates, and a lack of intensive angiogenesis andCAF networks.

In sum, highly prevalent MF profiles revealed in a large cohort (n=470)of melanoma patients suggested that melanoma tumors comprised arestricted number of principal variants in terms of their functionalorganization, which includes a pro-tumor microenvironment in dynamicequilibrium with an anti-tumor immune microenvironment.

Characteristics of melanoma tumor Types A-D (1^(st)-4^(th) MF profileclusters, respectively) were correlated with patient survival, intensityof cell infiltration (e.g., immune cells, stromal cells, andinflammatory cells), and tumor vascularization to provide a briefdescription of the four melanoma MF profiles. The brief description ofthe identified MF profiles in terms of a treatment perspective (e.g.,good, optimal, poor) and cellular infiltrate (e.g., immune, vascular,fibrotic) were:

-   -   A—good (immune, vascular, fibrotic);    -   B—optimal (immune);    -   C—poor (immunosuppressive, vascular, fibrotic); and    -   D—poor (immune “desert”).

Example 4: Four General Types of MF Profiles were Revealed ThroughoutDifferent Cancers

To determine whether the MF profile classification method displayedtissue-specificity, t-distributed stochastic neighbor embedding (tSNE)analysis was performed on 20 epithelial cancers (n=7920, TCGA) overprocess activity values (ES scores). This analysis showed that processactivity values formed distinct tissue of origin specific sample subsets(FIG. 43A). A common cluster for colon (COAD) and rectal (READ) cancerswas coherent with the current view that they have similar molecular andcellular origins. In order to minimize cancer specificity, processactivity values were normalized by Z-score transformation within eachcancer type. Following such normalization the MF profiles formed auniform single set in tSNE analysis (FIG. 43B).

Methods of building tumor MF profiles as described herein were appliedto carcinomas of different tissue origins. Using NGS data of cancerpatients available from TCGA, unsupervised dense subgraph clusteringanalysis was performed and tumor MF profiles for 7920 patients with 20different epithelial cancers were reconstructed. Among the differentcarcinoma patients studied, four prevalent types of molecular-functionalorganization were identified that were strikingly similar to theorganization of MF profiles for melanoma. The relative sizes of A, B, Cand D clusters varied among cancer types (FIG. 43C). The four MF profiletypes were also clearly evident in the analysis of the combined datasetof 20 cancer types (FIG. 43E). Similar results were obtained by thek-means pan-cancer clustering algorithm (FIGS. 44A-44G).

MF profile types for the different cancers and patient survival wereevaluated. The Type C (e.g., immunosuppression, vascular, fibrotic)cluster of carcinoma patients was linked with the poorest overallsurvival, while the Type B (e.g., immune) cluster had the best prognosis(FIG. 43F). These results were similar to what was seen in the melanomacohort.

The molecular-functional organization of non-epithelial neoplasmsincluding sarcoma, glioblastoma and glioma were analyzed. This analysisrevealed that glioblastoma and glioma (FIGS. 45A-45B), and sarcoma(FIGS. 45C-45D) types can be classified in a manner that is similar tocarcinomas. However, Types A-D (1^(st)-4^(th) MF profile clusters,respectively) of the analyzed non-epithelial cancers demonstrated a setof distinct molecular processes discriminating one type from another(FIGS. 45B and 45D).

Example 5: Tumor Type as a Basis for Response to Immune and TargetedTherapies

An MF profile was created to be a personalized picture of patient'stumor microenvironment. Therefore, it could be used as a basis tounderstand the influence of microenvironment on the efficacy ofdifferent therapies.

To examine whether the four prevalent types of tumor organization areindicative of a patient's response to certain therapies, the linkbetween a patient's MF profile and response to therapy was analyzed.

Efficacy of immune checkpoint blockage therapy (e.g., anti-CTLA-4 andanti-PD-1) is dependent on the amount of active immune infiltrate in thetumor microenvironment and tumor antigenicity. The expression levels ofimmune checkpoint inhibitor molecules cannot predict the efficacy ofcheck point blockade therapy on their own. On the concatenated datasetsof patients treated with anti-CTLA-4 (Nathanson et al., 2016; Van Allenet al., 2015) it was determined that patients with tumor MF-types A andB (first and second MF profile clusters, respectively) having highintratumoral immune content and high mutational burden were more likelyto respond to therapy (FIG. 46A). However, patients with immunesuppressive fibrotic MF-type C tumors (the third MF profile cluster)appeared to be completely non-responsive, regardless of mutation load intheir tumors. See Nathanson T et al. Somatic Mutations and NeoepitopeHomology in Melanomas Treated with CTLA-4 Blockade. Cancer Immunol Res.2017 January; 5(1):84-91. See also Van Allen E M et al. Genomiccorrelates of response to CTLA-4 blockade in metastatic melanoma.Science. 2015 Oct. 9; 350(6257):207-211. Each of the foregoingreferences are incorporated herein by reference in their entirety.

Analysis of the cohort of patients treated by anti-PD1 (Hugo et al.)yielded similar results. See Hugo W et al. Genomic and TranscriptomicFeatures of Response to Anti-PD-1 Therapy in Metastatic Melanoma. Cell.2016 Mar. 24; 165(1):35-44, which is hereby incorporated by reference inits entirety. Patients having tumor type C did not respond to therapy(FIG. 46B). MF profile B patients responded to immunotherapyindependently of their tumor mutation status (FIG. 46B), which isconsistent with the characterization of Type B tumors as having lowlevels of pro-tumor angiogenic and fibrotic activities. However, theanalysis revealed that type A tumors which have increased levels of bothimmune and fibrotic processes need to possess high mutational burden toincrease the probability of response. It is determined that tumors withhigh immune content (e.g., type A) may also contain many highlysuppressive cancer-associated fibroblasts that suppress T cellactivation independently of T-cell checkpoint inhibition mechanisms.

Application of MF profiles to datasets of patients treated withanti-CTLA-4 and anti-PD-1 therapies resulted in 0.72 and 0.76 AUC scoresfor response prediction respectively (FIG. 46F). With regard to responseto treatment, patients with type B tumors displayed the most favorablesurvival rate, and patients with type C tumors displayed unfavorablesurvival rates (FIGS. 46G-46H).

The efficacy of therapeutic MAGEA3-vaccine use depended on the tumorMF-type (FIG. 46F; AUC score of 0.72). As similarly determined forcheckpoint inhibitor therapies, type B tumors are associated withincreased response to vaccination (FIG. 46C). Non-responders had “immunedesert” type D tumors, which have no immune infiltration to driveanti-tumor immune response (FIG. 46C).

MF profiles were also associated with a targeted therapy outcome. Anindividual patient's mutational status seemed to be the most importantcharacteristic when choosing appropriate targeted therapy, but sometargeted therapies also affected the tumor microenvironment and thusresponse to them was found to strongly depend on the tumor organization.

Patients treated with cetuximab (EGFR inhibitor) from two independentcohorts (GSE5851, GSE65021) were arranged by tumor type from the mostimmune to the least immune (B, A, D, C) and stratified by EGFRexpression status. Patients with tumor types A and B (first and secondMF profile clusters, respectively) were unlikely to benefit fromanti-EGFR therapy (FIG. 46D). On the contrary, among patients with tumortypes C and D (third and fourth MF profile clusters, respectively) thenumber of responders was higher (FIG. 46D). These tumor types seemed tobe strongly dependent on the activity of growth factors which act viaEGFR. In fact, the combination of the tumor MF-type classification(types D and C) with EGFR-expression status increased responseprediction up to 80% of patients and had an overall AUC score of 0.8(FIGS. 46D and 46F).

The above examples show that additional personalization of the patienttumor MF profile by combining the tumor type with traits like mutationalburden or EGFR expression status lead to the possibility of usingportraits in different cancers and for prediction of response to varioustherapies.

Alternatively, just one of the processes comprised in the tumor MFprofile could serve a key predictor of certain therapy effectiveness.For instance, treatment efficacy of sunitinib, a pan-tyrosine kinaseinhibitor, was dependent only on tumor proliferation rate (AUC score0.91) which constitutes a single process of MF profile (FIGS. 46E-46Fand FIG. 46I).

Example 6: Dynamic Evolution of Tumor MF Profile Predicts Response toImmune Checkpoint Inhibitors

A dataset of 3 non-responder and 2 responder melanoma patients treatedwith anti-PD1 therapy whose tumors were measured before and aftertreatment was obtained. The dynamics of each patient's tumor wereplotted on a map created using 470 melanoma patients (TCGA).

Pre-treatment tumors for three non-responding patients were classifiedaccording to their MF profiles as type C (Pt3, Pt4) and type A (Pt5)tumors. These tumor types were associated with low mutation burden,which, according to the analysis of Hugo et al. dataset, was associatedwith the absence of response. Evolution of non-responder tumors can beclearly seen on the map of melanoma patients plotted on the PCA andcolored according to MF profile types (FIG. 47A). The non-respondingpatients (Pt3, Pt4, Pt5) move deeply into the type C tumors thatconstitute a “bad” zone of non-responders according to the analysis ofthe Hugo et al. dataset (FIGS. 47A-47B).

Alternatively, responder tumors were classified as immune enriched typeB (Pt2) and immune desert type D (Pt1) with high and mid number ofmutations before treatment. After the treatment responder tumors movedeven further to the zone of “best responders” of tumor types B (Pt1) andA (Pt2) (FIG. 47A). In a type D patient (Pt1) the number of immune cellswere increased, and the patient's tumor became type B (FIG. 47B).Receiver operating characteristics for therapy response prediction basedon tumor classification before treatment with AUC scores was determined(FIG. 47C).

Example 7: Application of Tumor MF Profiles for Personalized CombinationTherapy Design

Tumor molecular-functional portraits (MF profiles) can facilitatedevelopment of combination therapies. For example, if there is novisible tumor infiltration with MDSC, there is no reason to use remediesdirected against MDSC. Conversely, when there are clear signs in thetumor of an overexpressed vascular network this indicates a reasonabledemand for anti-angiogenic agents to be applied during treatment. Inaddition, if a functional module is substantial, then a therapy directedat regulating that functional module may be selected. In anotherexample, if a tumor module is absent, then a therapy can be selected toinduce the appearance of the module, should an appropriate inducerexist.

To facilitate development of specific combination therapies, the MFprofile was complemented with a list of known pharmaceutical compoundsdirected to particular functional modules (FIGS. 48A-48D). The MFprofile enabled the user to specify which functional module to target.Ultimately, it is the clinician's decision whether or not to use aparticular option, and if so, how to use it. The schematic shown inFIGS. 48A-48D is a representation of the interface created in order toallow a user to objectively evaluate a patient's tumor for the presenceor absence of primary molecular and cellular targets for the existingmodes of therapeutic intervention. Importantly, the schematic readilydiscards those remedies that would be irrelevant to this particularpatient because of the absence of functional modules to which theseremedies are directed.

Prevalent MF profiles for Types A-D (1^(st)-4^(th) MF profile clusters,respectively) tumors form the basis for designing therapeutic protocolsrelevant to each of these four tumor types. Described herein arepre-compiled combination therapy designs for tumors having MF profilesof types A, B, C and D (1^(st)-4^(th) MF profile clusters,respectively), starting with the latter as having the simplestmolecular-functional organization.

Type D tumors (the fourth MF profile cluster) represent the simplest MFprofile that are nearly devoid of any modules but have an increasedexpression of cancer signaling pathways and a high proliferativeactivity of malignant cells (FIG. 42D). For patients with Type D tumors,one can apply a chemotherapeutic regimen, radiotherapy, targetedtyrosine kinase, or cyclin-dependent kinase inhibitors to block celldivisions, but most probably none of these standard care therapies willbe curative and tumors will likely recur. In such patients, there is aneed to evoke the immune system as a way of destroying the tumor cellvariants that could escape conventional therapies. Treatment optionsthat would effectively attract cytotoxic T cells, Th1, and NK cells intothe tumor could be useful for such “non-inflamed” cancers.

Type C tumors were also identified to be “uninflamed” or “non-inflamed”(FIG. 42C). At the same time, they have increased expression of cancersignaling pathways and/or metastatic capabilities. Type C tumors arealso characterized by the prominence of tumor-promoting CAFs, anextensively developed network of tumor vasculature, and increasedexpression of tumor-promoting cytokines. In addition, myeloid lineagecompartments (MDSC, granulocytes, M2 macrophages) that greatly promotetumor progression are pronounced in Type C tumors. Accordingly, whendesigning a combination therapy for Type C tumor patients (FIG. 48B),therapies interfering with the refined cancer-signaling pathways, aswell as inhibitors of angiogenesis, CAFs and/or immunosuppressivefactors (e.g., TGFβ) that are produced by these cells would likely beused. In addition, remedies that are capable of M2 macrophages and MDSCreprogramming would likely be useful in combination therapies forpatients having Type C tumors.

Treatment strategies for “inflamed” tumors are more multifarious. Forpatients with Type B tumors (FIG. 48C) checkpoint inhibitors incombination with blockaders of Treg, MDSC and immunosuppressive (e.g.,TGFβ, IDO-1) factors could be used. Compared to Type B (second type)tumors, Type A (first type) tumors (FIG. 42A) require the addition ofangiogenesis and CAF inhibitors. As the infiltrating T cell compartmentsare well expressed in Types A and B (first and second type tumors,respectively) tumors, they could be fully exploited by the applicationof either personalized vaccines or vaccines based on the sharedtumor-specific antigens, or both. In addition, for the treatment ofTypes A and B tumors (first and second type, respectively), acombination of the referenced therapies with therapies that inhibitmetastatic or growth-signaling activities of malignant cells, ortherapies that block the action of tumor growth factors if they areprominently expressed in the particular patient's MF profile could beused.

Therapeutic combinations compiled for Types A, B, C and D tumors(first-fourth type tumors, respectively) can be adapted for a particularpatient. The identified and described cancer MF profiles provide anobjective basis for choosing a functionally relevant combination oftherapeutic components. Specific combinations of therapies for Types A-D(1^(st)-4^(th) type, respectively) tumors (“treatment standards”) can bepre-designed and tuned by adding or excluding certain remedies based onthe unique characteristics of a patient's tumor.

Given the efficiency and wide application of targeted inhibitors, the MFprofiles were further expanded to include a mutation status module thatrepresented the most important recurrent and therapy relevant mutationsin oncogenes. The presence of these mutations may be used as a biomarkerfor selecting targeted inhibitors. In another example, a MF profile wasmodified to design a combination therapy that included targetedinhibitors, relevant to the driver mutation (KIT) found in the tumor ofa melanoma patient (FIG. 48D). Identified mutations could also provideuseful information for designing of personalized neoantigen vaccine.

Example 8: MF Profile Complexity

The degree of detail of MF profiles was decreased from 28 modules (FIG.49A) down to 19 modules (FIG. 49B) or 5 modules (FIG. 49C) by collapsingrelated functional processes. A reduction of model complexity wasachieved by merging inherently related modules. For example, the T cell,T cell traffic and Cytotoxic T cell modules were be merged with NK cellmodule in the combined Effector T and NK cell module. Similarly, Th2cells, M2 macrophages and Pro-tumor cytokines were joined within theTumor-promoting immune infiltrate module.

The level of detail can be selected depending on the task. For example,the most simplified MF profile can serve to classify a tumor principaltype, e.g., “non-inflamed” or “inflamed,” or having extensiveangiogenesis, or containing an excess of cancer-associated fibroblasts,or exhibiting hypertrophy of suppressor cells. A more sophisticated MFprofile can be employed for the refined analysis of a tumor functionalorganization, specifically to identify the composition of infiltratingimmune cells, intensity of anticancer cytotoxic mechanisms, types ofimmunosuppressive cells and molecules, the number, differentiation phaseand activity of CAFs, and finally, malignancy details of cancerouscells.

Example 9: Analysis of Relationships Between Functional Modules

The MF profiles of Types A-D tumors (1^(st)-4^(th) type tumors,respectively) differ by the intensity of processes assigned to 28functional modules. These processes reflect the presence and functionalactivity of certain cell types—malignant, endothelial, fibroblasts, aswell as leukocytes/lymphocytes of various differentiation lineages. Thepresence and functional status of each cell type influences the presenceand function of other cell types in the tumor microenvironment, which inturn influences the presence and function of the former cell type.

Three principal variants of mutual interaction among cell types occur,and thus influences interaction between functional modules. The firstvariant is a synergistic action of two particular cell types or twofunctional modules, which means that activation of one module promotesactivation of the other. The second variant is an antagonism of twoparticular cell types or two functional modules, when activation of onemodule suppresses the other module. The third variant is the absence ofany mutual influence of two modules on each other.

In the case of a positive relationship, functionally connected modulescould be either co-activated or co-extinguished (e.g., not activated).The intensity of antagonistic functional modules should be theopposite—one active, the other not. In the absence of mutual influence,modules should randomly vary from portrait to portrait with no signs ofconnection.

Pearson correlation analysis of relationships between any two of the 28functional modules revealed two groups of modules bound by positivecorrelations (FIGS. 50A-50C). Modules in the first group having apositive relationship included T cell signatures, T cell trafficking,Th1 cells, Effector T cells, NK cells, MHC class II expression as wellas the Checkpoint inhibition and Treg modules. With lower positivecorrelation coefficients, MHC Class I, Co-activation molecules,Anti-tumor cytokines and B cells modules were adjacent to this group ofmodules. Modules of this first group were generally related to effectiveanti-tumor responses. Remarkably, these modules had negative correlationwith the tumor malignancy modules such as the Tumor proliferation rateand Cancer signaling (RAS/RAF/MEK) modules. In other words, the worsethe malignancy of the tumor, the less developed the immune responseswere within the tumor. Conversely, if the tumor had no prominentmalignancy signs, an intensive immune response was observed.

Modules in the second group having an antagonistic relationship includedCAFs, Angiogenesis and the Tumor-promoting growth factors, as well asPro-tumor cytokines, M2 macrophages, Granulocytes and MDSC modules. Thissecond group of modules functions to promote tumor growth, survival andmetastasis while suppressing immune responses that control tumoroutgrowth. In addition, this second group of modules were negativelycorrelated with cancer signaling (RAS/RAF/MEK) and proliferationmodules.

Taken together, the relationship between modules suggested that tumorscomprising driver mutations and/or high proliferation rates were devoidof tumor immune defenses and tumor-promoting modules (e.g., CAFs,angiogenesis, M2, MDSC). Thus, the malignancy of certain cells forms abasis for the development of “non-inflamed” tumors, wherein the activityof the microenvironment is reduced to a minimum.

EXAMPLE EMBODIMENTS

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups, the set of genegroups comprising gene groups associated with cancer malignancy anddifferent gene groups associated with cancer microenvironment; andidentifying, from among multiple MF profile clusters, an MF profilecluster with which to associate the MF profile for the subject, the MFprofile clusters comprising: a first MF profile cluster associated withinflamed and vascularized biological samples and/or inflamed andfibroblast-enriched biological samples, a second MF profile clusterassociated with inflamed and non-vascularized biological samples and/orinflamed and non-fibroblast-enriched biological samples, a third MFprofile cluster associated with non-inflamed and vascularized biologicalsamples and/or non-inflamed and fibroblast-enriched biological samples,and a fourth MF profile cluster associated with non-inflamed andnon-vascularized biological samples and/or non-inflamed andnon-fibroblast-enriched biological samples, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using RNA expression data obtained frombiological samples from the plurality of subjects, each of the pluralityof MF profiles containing a gene group expression level for each genegroup in the set of gene groups; and clustering the plurality of MFprofiles to obtain the MF profile clusters.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining RNA expressiondata and/or whole exome sequencing (WES) data for a biological samplefrom a subject; determining a molecular-functional (MF) profile for thesubject at least in part by determining, using the RNA expression data,a gene group expression level for each gene group in a set of genegroups, the set of gene groups comprising gene groups associated withcancer malignancy and different gene groups associated with cancermicroenvironment; and identifying, from among multiple MF profileclusters, an MF profile cluster with which to associate the MF profilefor the subject, the MF profile clusters comprising: a first MF profilecluster associated with inflamed and vascularized biological samplesand/or inflamed and fibroblast-enriched biological samples, a second MFprofile cluster associated with inflamed and non-vascularized biologicalsamples and/or inflamed and non-fibroblast-enriched biological samples,a third MF profile cluster associated with non-inflamed and vascularizedbiological samples and/or non-inflamed and fibroblast-enrichedbiological samples, and a fourth MF profile cluster associated withnon-inflamed and non-vascularized biological samples and/or non-inflamedand non-fibroblast-enriched biological samples, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using RNA expression data obtained frombiological samples from the plurality of subjects, each of the pluralityof MF profiles containing a gene group expression level for each genegroup in the set of gene groups; and clustering the plurality of MFprofiles to obtain the MF profile clusters.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause at least one computer hardware processor to perform:obtaining RNA expression data and/or whole exome sequencing (WES) datafor a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups, the set of genegroups comprising gene groups associated with cancer malignancy anddifferent gene groups associated with cancer microenvironment; andidentifying, from among multiple MF profile clusters, an MF profilecluster with which to associate the MF profile for the subject, the MFprofile clusters comprising: a first MF profile cluster associated withinflamed and vascularized biological samples and/or inflamed andfibroblast-enriched biological samples, a second MF profile clusterassociated with inflamed and non-vascularized biological samples and/orinflamed and non-fibroblast-enriched biological samples, a third MFprofile cluster associated with non-inflamed and vascularized biologicalsamples and/or non-inflamed and fibroblast-enriched biological samples,and a fourth MF profile cluster associated with non-inflamed andnon-vascularized biological samples and/or non-inflamed andnon-fibroblast-enriched biological samples, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using RNA expression data obtained frombiological samples from the plurality of subjects, each of the pluralityof MF profiles containing a gene group expression level for each genegroup in the set of gene groups; and clustering the plurality of MFprofiles to obtain the MF profile clusters.

In some embodiments, the gene groups associated with cancer malignancyis the tumor properties group; and the MF profile for the subjectcomprises determining a gene group expression level for the tumorproperties group. In some embodiments, the gene groups associated withcancer microenvironment are the tumor-promoting immune microenvironmentgroup, the anti-tumor immune microenvironment group, the angiogenesisgroup, and the fibroblasts group; and determining the MF profile for thesubject comprises determining a gene group expression level for each ofthe tumor-promoting immune microenvironment group, the anti-tumor immunemicroenvironment group, the angiogenesis group, and the fibroblastsgroup. In some embodiments, the gene groups associated with cancermalignancy comprise at least three genes from the following group: thetumor properties group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA,AURKB, CDK4, CDK6, PRC1, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, MCM6,PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, AKT3,BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, MKNK2, ALK, AXL, KIT, EGFR,ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL,PDGFRA, PDGFRB, NGF, CSF3, CSF2, FGF7, IGF1, IGF2, IL7, FGF2, TP53,SIK1, PTEN, DCN, MTAP, AIM2, RB1, ESRP1, CTSL, HOXA1, SMARCA4, SNAI2,TWIST1, NEDD9, PAPPA, HPSE, KISS1, ADGRG1, BRMS1, TCF21, CDH1, PCDH10,NCAM1, MITF, APC, ARID1A, ATM, ATRX, BAP1, BRAF, BRCA2, CDH1, CDKN2A,CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3, HRAS, IDH1, KRAS,MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS, PBRM1, PIK3CA,PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, and VHL.

In some embodiments, determining the MF portrait comprises: determiningthe gene group expression level for the tumor properties group using thegene expression level obtained from the RNA sequence data for at leastthree genes in the tumor properties group. In certain embodiments, thegene groups associated with cancer microenvironment comprise at leastthree genes from each of the following groups: the anti-tumor immunemicroenvironment group: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, HLA-DRA,HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1,HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, HLA-DRB6,CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, CD28, IFNG, GZMA, GZMB, PRF1,LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B, NKG7, CD160,CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1,KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5, CXCL9, CXCL10, CXCR3, CX3CL1, CCR7,CXCL11, CCL21, CCL2, CCL3, CCL4, CCL5, EOMES, TBX21, ITK, CD3D, CD3E,CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, TRAT1, CD19, MS4A1, TNFRSF13C,CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, NOS2,IL12A, IL12B, IL23A, TNF, IL1B, SOCS3, IFNG, IL2, CD40LG, IL15, CD27,TBX21, LTA, IL21, HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG;the tumor-promoting immune microenvironment group: PDCD1, CD274, CTLA4,LAG3, PDCD1LG2, BTLA, HAVCR2, VSIR, CXCL12, TGFB1, TGFB2, TGFB3, FOXP3,CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5,CXCL13, CCL28, IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB,CXCR4, CD33, CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1,CXCL8, CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3,CCL26, PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1,TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, CTSG,IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1,CD163, CSF1R, IL4, IL5, IL13, IL10, IL25, GATA3, IL10, TGFB1, TGFB2,TGFB3, IL22, MIF, CFD, CFI, CD55, CD46, and CR1; the fibroblasts group:LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2, TGFB3, ACTA2,FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, and COL6A3; and the angiogenesisgroup: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR,ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1, MMRN1, LDHA, HIF1A,EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMPS. In some embodiments,determining the MF portrait comprises: determining the gene groupexpression level for the anti-tumor immune microenvironment group usingthe gene expression level obtained from the RNA sequence data for atleast three genes in the anti-tumor immune microenvironment group;determining the gene group expression level for the tumor-promotingimmune microenvironment group using the gene expression level obtainedfrom the RNA sequence data for at least three genes in thetumor-promoting immune microenvironment group; determining the genegroup expression level for the fibroblasts group using the geneexpression level obtained from the RNA sequence data for at least threegenes in the fibroblasts group; and determining the gene groupexpression level for the angiogenesis group using the gene expressionlevel obtained from the RNA sequence data for at least three genes inthe angiogenesis group. In certain embodiments, the gene groupsassociated with cancer malignancy are: the proliferation rate group, thePI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, thereceptor tyrosine kinases expression group, the tumor suppressors group,the metastasis signature group, the anti-metastatic factors group, andthe mutation status group; and determining the MF profile for thesubject comprises determining a gene group expression level for each ofthe proliferation rate group, the PI3K/AKT/mTOR signaling group, theRAS/RAF/MEK signaling group, the receptor tyrosine kinases expressiongroup, the tumor suppressors group, the metastasis signature group, theanti-metastatic factors group, and the mutation status group. In someembodiments, the gene groups associated with cancer microenvironmentare: the antigen presentation group, the cytotoxic T and NK cells group,the B cells group, the anti-tumor microenvironment group, the checkpointinhibition group, the Treg group, the MDSC group, the granulocytesgroup, the cancer associated fibroblasts group, the angiogenesis group,and the tumor-promotive immune group; and determining the MF profile forthe subject comprises determining a gene group expression level for eachof the antigen presentation group, the cytotoxic T and NK cells group,the B cells group, the anti-tumor microenvironment group, the checkpointinhibition group, the Treg group, the MDSC group, the granulocytesgroup, the cancer associated fibroblasts group, the angiogenesis group,and the tumor-promotive immune group.

In certain embodiments, the gene groups associated with cancermalignancy comprise at least three genes from each of the followinggroups: the proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1,CCNE1, AURKA, AURKB, CDK4, CDK6, PRC1, E2F1, MYBL2, BUB1, PLK1, CCNB1,MCM2, and MCM6; the PI3K/AKT/mTOR signaling group: PIK3CA, PIK3CB,PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, and AKT3; the RAS/RAF/MEKsignaling group: BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, and MKNK2; thereceptor tyrosine kinases expression group: ALK, AXL, KIT, EGFR, ERBB2,FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA,and PDGFRB; the tumor suppressors group: TP53, SIK1, PTEN, DCN, MTAP,AIM2, and RB1; the metastasis signature group: ESRP1, CTSL, HOXA1,SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA, and HPSE; the anti-metastaticfactors group: KISS1, ADGRG1, BRMS1, TCF21, CDH1, PCDH10, NCAM1, andMITF; and the mutation status group: APC, ARID1A, ATM, ATRX, BAP1, BRAF,BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3,HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS,PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, andVHL. In certain embodiments, determining the MF portrait comprises:determining the gene group expression level for the proliferation rategroup using the gene expression level obtained from the RNA sequencedata for at least three genes in the proliferation rate group;determining the gene group expression level for the PI3K/AKT/mTORsignaling group using the gene expression level obtained from the RNAsequence data for at least three genes in the PI3K/AKT/mTOR signalinggroup; determining the gene group expression level for the RAS/RAF/MEKsignaling group using the gene expression level obtained from the RNAsequence data for at least three genes in the RAS/RAF/MEK signalinggroup; determining the gene group expression level for the receptortyrosine kinases expression group using the gene expression levelobtained from the RNA sequence data for at least three genes in thereceptor tyrosine kinases expression group; determining the gene groupexpression level for the tumor suppressors group using the geneexpression level obtained from the RNA sequence data for at least threegenes in the tumor suppressors group; determining the gene groupexpression level for the metastasis signature group using the geneexpression level obtained from the RNA sequence data for at least threegenes in the metastasis signature group; determining the gene groupexpression level for the anti-metastatic factors group using the geneexpression level obtained from the RNA sequence data for at least threegenes in the anti-metastatic factors group; and determining the genegroup expression level for the mutation status group using the geneexpression level obtained from the RNA sequence data for at least threegenes in the mutation status group. In certain embodiments, the genegroups associated with cancer microenvironment comprise at least threegenes from each of the following groups: the cancer associatedfibroblasts group: LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2,TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, and COL6A3; theangiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1,PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1,MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3; theantigen presentation group: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2,HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1,HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2,HLA-DRB6, CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, and CD28; thecytotoxic T and NK cells group: IFNG, GZMA, GZMB, PRF1, LCK, GZMK,ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B, NKG7, CD160, CD244, NCR1,KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2,KIR2DS3, KIR2DS4, KIR2DS5, CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11,CCL21, CCL2, CCL3, CCL4, CCL5, EOMES, TBX21, ITK, CD3D, CD3E, CD3G,TRAC, TRBC1, TRBC2, LCK, UBASH3A, and TRAT1; the B cells group: CD19,MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A,CD79B, and BLK; the anti-tumor microenvironment group: NOS2, IL12A,IL12B, IL23A, TNF, IL1B, SOCS3, IFNG, IL2, CD40LG, IL15, CD27, TBX21,LTA, IL21, HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG; thecheckpoint inhibition group: PDCD1, CD274, CTLA4, LAGS, PDCD1LG2, BTLA,HAVCR2, and VSIR; the Treg group: CXCL12, TGFB1, TGFB2, TGFB3, FOXP3,CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5,CXCL13, and CCL28; the MDSC group: IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2,TGFB3, NOS2, CYBB, CXCR4, CD33, CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2,CCL3, CCL5, CSF1, and CXCL8; the granulocytes group: CXCL8, CXCL2,CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, CCL26, PRG2, EPX, RNASE2,RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1, TPSAB1, MS4A2, CPA3, IL4,IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, and CTSG; the tumor-promotiveimmune group: IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1,PTGS1, MSR1, CD163, CSF1R, IL4, IL5, IL13, IL10, IL25, GATA3, IL10,TGFB1, TGFB2, TGFB3, IL22, MIF, CFD, CFI, CD55, CD46, and CR1. Incertain embodiments, determining the MF portrait comprises: determiningthe gene group expression level for the cancer associated fibroblastsgroup using the gene expression level obtained from the RNA sequencedata for at least three genes in the cancer associated fibroblastsgroup; determining the gene group expression level for the angiogenesisgroup using the gene expression level obtained from the RNA sequencedata for at least three genes in the angiogenesis group; determining thegene group expression level for the antigen presentation group using thegene expression level obtained from the RNA sequence data for at leastthree genes in the antigen presentation group; determining the genegroup expression level for the cytotoxic T and NK cells group using thegene expression level obtained from the RNA sequence data for at leastthree genes in the cytotoxic T and NK cells group; determining the genegroup expression level for the B cells group using the gene expressionlevel obtained from the RNA sequence data for at least three genes inthe B cells group; determining the gene group expression level for theanti-tumor microenvironment group using the gene expression levelobtained from the RNA sequence data for at least three genes in theanti-tumor microenvironment group; determining the gene group expressionlevel for the checkpoint inhibition group using the gene expressionlevel obtained from the RNA sequence data for at least three genes inthe checkpoint inhibition group; determining the gene group expressionlevel for the Treg group using the gene expression level obtained fromthe RNA sequence data for at least three genes in the Treg group;determining the gene group expression level for the MDSC group using thegene expression level obtained from the RNA sequence data for at leastthree genes in the MDSC group; determining the gene group expressionlevel for the granulocytes group using the gene expression levelobtained from the RNA sequence data for at least three genes in thegranulocytes group; and determining the gene group expression level forthe tumor-promotive immune group using the gene expression levelobtained from the RNA sequence data for at least three genes in thetumor-promotive immune group.

In some embodiments, the gene groups associated with cancer malignancyare: the proliferation rate group, the PI3K/AKT/mTOR signaling group,the RAS/RAF/MEK signaling group, the receptor tyrosine kinasesexpression group, the growth factors group, the tumor suppressors group,the metastasis signature group, the anti-metastatic factors group, andthe mutation status group; and determining the MF profile for thesubject comprises determining a gene group expression level for each ofthe proliferation rate group, the PI3K/AKT/mTOR signaling group, theRAS/RAF/MEK signaling group, the receptor tyrosine kinases expressiongroup, the growth factors group, the tumor suppressors group, themetastasis signature group, the anti-metastatic factors group, and themutation status group. In certain embodiments, the gene groupsassociated with cancer microenvironment are: the cancer associatedfibroblasts group, the angiogenesis group, the MHCI group, the MHCIIgroup, the coactivation molecules group, the effector cells group, theNK cells group, the T cell traffic group, the T cells group, the B cellsgroup, the M1 signatures group, the Th1 signature group, the antitumorcytokines group, the checkpoint inhibition group, the Treg group, theMDSC group, the granulocytes group, the M2 signature group, the Th2signature group, the protumor cytokines group, and the complementinhibition group; and determining the MF profile for the subjectcomprises determining a gene group expression level for each of thecancer associated fibroblasts group, the angiogenesis group, the MHCIgroup, the MHCII group, the coactivation molecules group, the effectorcells group, the NK cells group, the T cell traffic group, the T cellsgroup, the B cells group, the M1 signatures group, the Th1 signaturegroup, the antitumor cytokines group, the checkpoint inhibition group,the Treg group, the MDSC group, the granulocytes group, the M2 signaturegroup, the Th2 signature group, the protumor cytokines group, and thecomplement inhibition group. In certain embodiments, the gene groupsassociated with cancer malignancy comprise at least three genes fromeach of the following groups: the proliferation rate group: MKI67,ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, CDK4, CDK6, PRC1, E2F1,MYBL2, BUB1, PLK1, CCNB1, MCM2, and MCM6; the PI3K/AKT/mTOR signalinggroup: PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2,and AKT3; the RAS/RAF/MEK signaling group: BRAF, FNTA, FNTB, MAP2K1,MAP2K2, MKNK1, and MKNK2; the receptor tyrosine kinases expressiongroup: ALK, AXL, KIT, EGFR, ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2,FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA, and PDGFRB; the growth factorsgroup: NGF, CSF3, CSF2, FGF7, IGF1, IGF2, IL7, and FGF2; the tumorsuppressors group: TP53, SIK1, PTEN, DCN, MTAP, AIM2, and RB1; themetastasis signature group: ESRP1, CTSL, HOXA1, SMARCA4, SNAI2, TWIST1,NEDD9, PAPPA, and HPSE; the anti-metastatic factors group: KISS1,ADGRG1, BRMS1, TCF21, CDH1, PCDH10, NCAM1, and MITF; and the mutationstatus group: APC, ARID1A, ATM, ATRX, BAP1, BRAF, BRCA2, CDH1, CDKN2A,CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3, HRAS, IDH1, KRAS,MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS, PBRM1, PIK3CA,PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, and VHL. In certainembodiments, determining the MF portrait comprises: determining the genegroup expression level for the proliferation rate group using the geneexpression level obtained from the RNA sequence data for at least threegenes in the proliferation rate group; determining the gene groupexpression level for the PI3K/AKT/mTOR signaling group using the geneexpression level obtained from the RNA sequence data for at least threegenes in the PI3K/AKT/mTOR signaling group; determining the gene groupexpression level for the RAS/RAF/MEK signaling group using the geneexpression level obtained from the RNA sequence data for at least threegenes in the RAS/RAF/MEK signaling group; determining the gene groupexpression level for the receptor tyrosine kinases expression groupusing the gene expression level obtained from the RNA sequence data forat least three genes in the receptor tyrosine kinases expression group;determining the gene group expression level for the growth factors groupusing the gene expression level obtained from the RNA sequence data forat least three genes in the growth factors group; determining the genegroup expression level for the tumor suppressors group using the geneexpression level obtained from the RNA sequence data for at least threegenes in the tumor suppressors group; determining the gene groupexpression level for the metastasis signature group using the geneexpression level obtained from the RNA sequence data for at least threegenes in the metastasis signature group; determining the gene groupexpression level for the anti-metastatic factors group using the geneexpression level obtained from the RNA sequence data for at least threegenes in the anti-metastatic factors group; and determining the genegroup expression level for the mutation status group using the geneexpression level obtained from the RNA sequence data for at least threegenes in the mutation status group.

In some embodiments, the gene groups associated with cancermicroenvironment comprise at least three genes from each of thefollowing groups: the cancer associated fibroblasts group: LGALS1,COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2, TGFB3, ACTA2, FGF2, FAP,LRP1, CD248, COL6A1, COL6A2, and COL6A3; the angiogenesis group: VEGFA,VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1,ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1, MMRN1, LDHA, HIF1A, EPAS1,CA9, SPP1, LOX, SLC2A1, and LAMPS; the MHCI group: HLA-A, HLA-B, HLA-C,B2M, TAP1, and TAP2; the MHCII group: HLA-DRA, HLA-DRB1, HLA-DOB,HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1,HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, and HLA-DRB6; the coactivationmolecules group: CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, and CD28; theeffector cells group: IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY,FASLG, TBX21, EOMES, CD8A, and CD8B; the NK cells group: NKG7, CD160,CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1,KIR2DS2, KIR2DS3, KIR2DS4, and KIR2DS5; the T cell traffic group: CXCL9,CXCL10, CXCR3, CX3CL1, CCR7, CXCL11, CCL21, CCL2, CCL3, CCL4, and CCL5;the T cells group: EOMES, TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1,TRBC2, LCK, UBASH3A, and TRAT1; the B cells group: CD19, MS4A1,TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, andBLK; the M1 signatures group: NOS2, IL12A, IL12B, IL23A, TNF, IL1B, andSOCS3; the Th1 signature group: IFNG, IL2, CD40LG, IL15, CD27, TBX21,LTA, and IL21; the antitumor cytokines group: HMGB1, TNF, IFNB1, IFNA2,CCL3, TNFSF10, and FASLG; the checkpoint inhibition group: PDCD1, CD274,CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, and VSIR; the Treg group: CXCL12,TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4,CCL22, CCL1, CCL2, CCL5, CXCL13, and CCL28; the MDSC group: IDO1, ARG1,IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB, CXCR4, CD33, CXCL1, CXCL5,CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, and CXCL8; the granulocytesgroup: CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3,CCL26, PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1,TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, andCTSG; the M2 signature group: IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1,CSF1, LRP1, ARG1, PTGS1, MSR1, CD163, and CSF1R; the Th2 signaturegroup: IL4, IL5, IL13, IL10, IL25, and GATA3; the protumor cytokinesgroup: IL10, TGFB1, TGFB2, TGFB3, IL22, and MIF; and the complementinhibition group: CFD, CFI, CD55, CD46, and CR1. In certain embodiments,determining the MF portrait comprises: determining the gene groupexpression level for the cancer associated fibroblasts group using thegene expression level obtained from the RNA sequence data for at leastthree genes in the cancer associated fibroblasts group; determining thegene group expression level for the angiogenesis group using the geneexpression level obtained from the RNA sequence data for at least threegenes in the angiogenesis group; determining the gene group expressionlevel for the MHCI group using the gene expression level obtained fromthe RNA sequence data for at least three genes in the MHCI group;determining the gene group expression level for the MHCII group usingthe gene expression level obtained from the RNA sequence data for atleast three genes in the MHCII group; determining the gene groupexpression level for the coactivation molecules group using the geneexpression level obtained from the RNA sequence data for at least threegenes in the coactivation molecules group; determining the gene groupexpression level for the effector cells group using the gene expressionlevel obtained from the RNA sequence data for at least three genes inthe effector cells group; determining the gene group expression levelfor the NK cells group using the gene expression level obtained from theRNA sequence data for at least three genes in the NK cells group;determining the gene group expression level for the T cell traffic groupusing the gene expression level obtained from the RNA sequence data forat least three genes in the T cell traffic group; determining the genegroup expression level for the T cells group using the gene expressionlevel obtained from the RNA sequence data for at least three genes inthe T cells group; determining the gene group expression level for the Bcells group using the gene expression level obtained from the RNAsequence data for at least three genes in the B cells group; determiningthe gene group expression level for the M1 signatures group using thegene expression level obtained from the RNA sequence data for at leastthree genes in the M1 signatures group; determining the gene groupexpression level for the Th1 signature group using the gene expressionlevel obtained from the RNA sequence data for at least three genes inthe Th1 signature group; determining the gene group expression level forthe antitumor cytokines group using the gene expression level obtainedfrom the RNA sequence data for at least three genes in the antitumorcytokines group; determining the gene group expression level for thecheckpoint inhibition group using the gene expression level obtainedfrom the RNA sequence data for at least three genes in the checkpointinhibition group; determining the gene group expression level for theTreg group using the gene expression level obtained from the RNAsequence data for at least three genes in the Treg group; determiningthe gene group expression level for the MDSC group using the geneexpression level obtained from the RNA sequence data for at least threegenes in the MDSC group; determining the gene group expression level forthe granulocytes group using the gene expression level obtained from theRNA sequence data for at least three genes in the granulocytes group;determining the gene group expression level for the M2 signature groupusing the gene expression level obtained from the RNA sequence data forat least three genes in the M2 signature group; determining the genegroup expression level for the Th2 signature group using the geneexpression level obtained from the RNA sequence data for at least threegenes in the Th2 signature group; determining the gene group expressionlevel for the protumor cytokines group using the gene expression levelobtained from the RNA sequence data for at least three genes in theprotumor cytokines group; and determining the gene group expressionlevel for the complement inhibition group using the gene expressionlevel obtained from the RNA sequence data for at least three genes inthe complement inhibition group.

In some embodiments, the system, method, or computer-readable storagemedium further comprises identifying at least one first therapy for thesubject based on the identified MF profile cluster. In some embodiments,identifying at least one first therapy consists of identifying a singletherapy. In some embodiments, identifying at least one first therapyconsists of identifying two or more therapies. In some embodiments,identifying the at least one therapy comprises identifying at least onetherapy selected from the group consisting of: chemotherapy, antibodydrug conjugates, hormonal therapy, viral therapy, genetic therapy,non-immune protein therapy, antiangiogenic agents, anti-cancer vaccines,radiotherapy, soluble receptor therapy, cell based therapies,immunotherapy, and targeted therapy. In certain embodiments, identifyingthe at least one therapy comprises identifying at least one therapyselected from the group consisting of: HGFR inhibitors, EGFR inhibitors,VEGF inhibitors, PDGF inhibitors, CXR2 inhibitors, CXCR4 inhibitors,DPP-4 inhibitors, galectin inhibitors, antifibrotic agents, LPR1inhibitors, TGF-beta inhibitors, IL5 inhibitors, IL4 inhibitors, IL13inhibitors, IL22 inhibitors, CSF1R inhibitors, IDO inhibitors, LPR1inhibitors, CD25 inhibitors, GITR inhibitors, PD1 inhibitors, CTLA1inhibitors, PDL1 inhibitors, LAG3 inhibitors, TIM3 inhibitors, vaccines,PRIMA-1 analogues, CD40 agonists, ICOS agonists, OX40 agonists, Bcl-2inhibitors, AKT inhibitors, MYC-targeting siRNA, pan-tyrosine kinaseinhibitors, CDK4/6 inhibitors, Aurora A inhibitors, vaccines, LAG3inhibitors, and any antibody-drug conjugate. In certain embodiments,identifying the at least one therapy comprises identifying at least onetherapy selected from the group consisting of: HGFR inhibitors, EGFRinhibitors, VEGF inhibitors, PDGF inhibitors, CXR2 inhibitors, galectininhibitors, antifibrotic agents, LPR1 inhibitors, TGF-beta inhibitors,IL5 inhibitors, IL4 inhibitors, IL13 inhibitors, IL22 inhibitors, CSF1Rinhibitors, IDO inhibitors, CXCR4 inhibitors, CD25 inhibitors, GITRinhibitors, PD1 inhibitors, CTLA1 inhibitors, PDL1 inhibitors, LAG3inhibitors, TIM3 inhibitors, and vaccines. In certain embodiments,identifying the at least one therapy comprises identifying at least onetherapy selected from the group consisting of: HGFR inhibitors, EGFRinhibitors, PRIMA-1 analogues, TGF-beta inhibitors, IL22 inhibitors,CSF1R inhibitors, IDO inhibitors, LPR1 inhibitors, CXCR4 inhibitors,CD25 inhibitors, GITR inhibitors, CD40 agonists, ICOS agonists, OX40agonists, and vaccines. In some embodiments, identifying the at leastone therapy comprises identifying at least one therapy selected from thegroup consisting of: Bcl-2 inhibitors, AKT inhibitors, MYC-targetingsiRNA, PRIMA-1 analogues, VEGF inhibitors, PDGF inhibitors, CXR2inhibitors, galectin inhibitors, antifibrotic agents, LPR1 inhibitors,TGF-beta inhibitors, IL5 inhibitors, IL4 inhibitors, IL13 inhibitors,CSF1R inhibitors, IDO inhibitors, CXCR4 inhibitors, and vaccines.

In some embodiments, identifying the at least one therapy comprisesidentifying at least one therapy selected from the group consisting of:antibody-drug conjugates, HGFR inhibitors, EGFR inhibitors, VEGFinhibitors, PDGF inhibitors, CXCR2 inhibitors, galectin inhibitors,antifibrotic agents, LPR1 inhibitors, TGF-beta inhibitors, IL22inhibitors, and CXCL10 disrupting inhibitors. In certain embodiments,identifying the at least one therapy comprises identifying at least onetherapy selected from the group consisting of: Bcl-2 inhibitors, AKTinhibitors, MYC-targeting siRNA, chemotherapy, pan-tyrosine kinaseinhibitors, CDK4/6 inhibitors, Aurora A inhibitors, and DPP-4inhibitors.

In some embodiments, obtaining the RNA expression data is performedusing whole transcriptome sequencing or mRNA sequencing. In certainembodiments, each of the biological samples is from a tumor or tissueknown or suspected of having cancerous cells.

In some embodiments, the system, method, or computer-readable storagemedium further comprises generating the MF profile clusters, thegenerating comprising: obtaining RNA expression data from biologicalsamples obtained from a plurality of subjects; determining a respectiveplurality of MF profiles for the plurality of subjects, each of theplurality of MF profiles containing a gene group expression level foreach gene group in the set of gene groups; and clustering the pluralityof MF profiles to obtain the MF profile clusters. In certainembodiments, clustering the plurality of MF profiles is performed byusing a k-means clustering technique.

In some embodiments, the system, method, or computer-readable storagemedium further comprises: determining at least one visual characteristicof a first graphical user interface (GUI) element using a first genegroup expression level for at least one gene group associated withcancer malignancy and at least one visual characteristic of a second GUIelement using a second gene group expression level for at least one genegroup associated with cancer microenvironment; generating a personalizedGUI personalized to the subject, the GUI comprising: a first portionassociated with cancer malignancy and containing the first GUI element;and a second portion associated with cancer microenvironment andcontaining the second GUI element, wherein the second portion isdifferent from the first portion; and presenting the generatedpersonalized GUI to a user. In some embodiments, determining the atleast one visual characteristic of the first GUI element comprisesdetermining size of the first GUI element using the first gene groupexpression level. In certain embodiments, determining the at least onevisual characteristic of the first GUI element comprises determiningcolor of the first GUI element using the first gene group expressionlevel. In certain embodiments, the first portion comprises a firstplurality of GUI elements representing a respective plurality of genegroups associated with cancer malignancy. In certain embodiments, thesecond portion comprises a second plurality of GUI elements representinga respective plurality of gene groups associated with cancermicroenvironment.

In some embodiments, the system, method, or computer-readable storagemedium further comprises: obtaining RNA expression data for at least oneadditional biological sample obtained from the subject subsequent toadministration of at least one first therapy; determining, using the RNAexpression data for at least one additional biological sample obtainedfrom the subject subsequent to administration of at least one therapy, asecond MF profile for the subject, wherein the second MF profile isdetermined at least in part by determining, using the RNA expressiondata for at least one additional biological sample obtained from thesubject subsequent to administration of at least one therapy, a genegroup expression level for each gene group in a set of gene groups, theset of gene groups comprising gene groups associated with cancermalignancy and different gene groups associated with cancermicroenvironment; and identifying, from among the MF profile clusters,an MF profile cluster with which to associate the MF profile for thesubject.

In certain embodiments, the system, method, or computer-readable storagemedium further comprises determining that the at least one first therapyis effectively treating the subject. In some embodiments, the system,method, or computer-readable storage medium further comprises:determining that the at least one first therapy is not effectivelytreating the subject; and identifying at least one second therapy forthe subject based on the second MF profile cluster. In certainembodiments, determining the MF profile for the subject comprises:determining a first gene group expression level for a first gene groupof the gene groups associated with cancer malignancy using a gene setenrichment analysis (GSEA) technique; and determining a second genegroup expression level for a second gene group of the gene groupsassociated with cancer microenvironment using the gene set enrichmentanalysis (GSEA) technique.

In some embodiments, determining the MF profile for the subjectcomprises: determining a first gene group expression level for a firstgene group of the gene groups associated with cancer malignancy using amutation count technique; and determining a second gene group expressionlevel for a second gene group of the gene groups associated with cancermicroenvironment using the mutation count technique. In someembodiments, the WES data is used to quantify tumor burden (purity),identify specific mutations, and/or to calculate the number ofneoantigens.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data from biological samples from a plurality of subjects, atleast some of the subjects having a cancer of a particular type;determining a respective plurality of molecular-functional (MF) profilesfor the plurality of subjects at least in part by, for each of theplurality of subjects, determining, using the RNA expression data, arespective gene group expression level for each group in a set of genegroups, the set of gene groups comprising gene groups associated withcancer malignancy and different gene groups associated with cancermicroenvironment; clustering the plurality of MF profiles to obtain MFprofile clusters comprising: a first MF profile cluster associated withinflamed and vascularized biological samples and/or inflamed andfibroblast-enriched biological samples, a second MF profile clusterassociated with inflamed and non-vascularized biological samples and/orinflamed and non-fibroblast-enriched biological samples, a third MFprofile cluster associated with non-inflamed and vascularized biologicalsamples and/or non-inflamed and fibroblast-enriched biological samples,and a fourth MF profile cluster associated with non-inflamed andnon-vascularized biological samples and/or non-inflamed andnon-fibroblast-enriched biological sample; and storing the plurality ofMF profiles in association with information identifying the particularcancer type.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining RNA expressiondata and/or whole exome sequencing (WES) data from biological samplesfrom a plurality of subjects, at least some of the subjects having acancer of a particular type; determining a respective plurality ofmolecular-functional (MF) profiles for the plurality of subjects atleast in part by, for each of the plurality of subjects, determining,using the RNA expression data, a respective gene group expression levelfor each group in a set of gene groups, the set of gene groupscomprising gene groups associated with cancer malignancy and differentgene groups associated with cancer microenvironment; clustering theplurality of MF profiles to obtain MF profile clusters comprising: afirst MF profile cluster associated with inflamed and vascularizedbiological samples and/or inflamed and fibroblast-enriched biologicalsamples, a second MF profile cluster associated with inflamed andnon-vascularized biological samples and/or inflamed andnon-fibroblast-enriched biological samples, a third MF profile clusterassociated with non-inflamed and vascularized biological samples and/ornon-inflamed and fibroblast-enriched biological samples, and a fourth MFprofile cluster associated with non-inflamed and non-vascularizedbiological samples and/or non-inflamed and non-fibroblast-enrichedbiological sample; and storing the plurality of MF profiles inassociation with information identifying the particular cancer type.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause at least one computer hardware processor to perform:obtaining RNA expression data and/or whole exome sequencing (WES) datafrom biological samples from a plurality of subjects, at least some ofthe subjects having a cancer of a particular type; determining arespective plurality of molecular-functional (MF) profiles for theplurality of subjects at least in part by, for each of the plurality ofsubjects, determining, using the RNA expression data, a respective genegroup expression level for each group in a set of gene groups, the setof gene groups comprising gene groups associated with cancer malignancyand different gene groups associated with cancer microenvironment;clustering the plurality of MF profiles to obtain MF profile clusterscomprising: a first MF profile cluster associated with inflamed andvascularized biological samples and/or inflamed and fibroblast-enrichedbiological samples, a second MF profile cluster associated with inflamedand non-vascularized biological samples and/or inflamed andnon-fibroblast-enriched biological samples, a third MF profile clusterassociated with non-inflamed and vascularized biological samples and/ornon-inflamed and fibroblast-enriched biological samples, and a fourth MFprofile cluster associated with non-inflamed and non-vascularizedbiological samples and/or non-inflamed and non-fibroblast-enrichedbiological sample; and storing the plurality of MF profiles inassociation with information identifying the particular cancer type.

In some embodiments, the system, method, or computer-readable storagemedium further comprises: obtaining RNA expression data for at least onebiological sample obtained from an additional subject; determining,using the RNA expression data for the at least one additional biologicalsample obtained from the additional subject, an MF profile for theadditional subject, wherein the MF profile for the additional subject isdetermined at least in part by determining, using the RNA expressiondata for the at least one additional biological sample obtained from theadditional subject, a gene group expression level for each gene group ina set of gene groups, the set of gene groups comprising gene groupsassociated with cancer malignancy and different gene groups associatedwith cancer microenvironment; and identifying, from among the MF profileclusters, an MF profile cluster with which to associate the MF profilefor the additional subject.

In some embodiments, the system, method, or computer-readable storagemedium further comprises: determining at least one visual characteristicof a first graphical user interface (GUI) element using a first genegroup expression level for at least one gene group associated withcancer malignancy and at least one visual characteristic of a second GUIelement using a second gene group expression level for at least one genegroup associated with cancer microenvironment; generating a personalizedGUI personalized to the additional subject, the GUI comprising: a firstportion associated with cancer malignancy and containing the first GUIelement; and a second portion associated with cancer microenvironmentand containing the second GUI element, wherein the second portion isdifferent from the first portion; and presenting the generatedpersonalized GUI to a user.

In certain embodiments, the first portion comprises a first plurality ofGUI elements representing a respective plurality of gene groupsassociated with cancer malignancy. In certain embodiments, the secondportion comprises a second plurality of GUI elements representing arespective plurality of gene groups associated with cancermicroenvironment. In some embodiments, determining the respective genegroup expression level for each group in the set of gene groups isperformed using a gene set enrichment analysis (GSEA) technique. In someembodiments, determining the respective gene group expression level foreach group in the set of gene groups is performed using a mutation counttechnique. In certain embodiments, the clustering is performed using acommunity detection clustering technique. In certain embodiments, theclustering is performed using a k-means clustering technique.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups, the set of genegroups comprising a first gene group associated with cancer malignancyand a second gene group associated with cancer microenvironment, whereinthe first and second gene groups are different, the determiningcomprising: determining a first gene group expression level for thefirst gene group, and determining a second gene group expression levelfor the second gene group; determining a first visual characteristic fora first graphical user interface (GUI) element using the first genegroup expression level; determining a second visual characteristic for asecond GUI element using the second gene group expression level;generating a personalized GUI personalized to the subject, the GUIcomprising: a first GUI portion associated with cancer malignancy andcontaining the first GUI element having the first visual characteristic,and a second GUI portion associated with cancer microenvironment andcontaining the second GUI element having the second visualcharacteristic; and presenting the generated personalized GUI to a user.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining RNA expressiondata and/or whole exome sequencing (WES) data for a biological samplefrom a subject; determining a molecular-functional (MF) profile for thesubject at least in part by determining, using the RNA expression data,a gene group expression level for each gene group in a set of genegroups, the set of gene groups comprising a first gene group associatedwith cancer malignancy and a second gene group associated with cancermicroenvironment, wherein the first and second gene groups aredifferent, the determining comprising: determining a first gene groupexpression level for the first gene group, and determining a second genegroup expression level for the second gene group; determining a firstvisual characteristic for a first graphical user interface (GUI) elementusing the first gene group expression level; determining a second visualcharacteristic for a second GUI element using the second gene groupexpression level; generating a personalized GUI personalized to thesubject, the GUI comprising: a first GUI portion associated with cancermalignancy and containing the first GUI element having the first visualcharacteristic, and a second GUI portion associated with cancermicroenvironment and containing the second GUI element having the secondvisual characteristic; and presenting the generated personalized GUI toa user.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause at least one computer hardware processor to perform:obtaining RNA expression data and/or whole exome sequencing (WES) datafor a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups, the set of genegroups comprising a first gene group associated with cancer malignancyand a second gene group associated with cancer microenvironment, whereinthe first and second gene groups are different, the determiningcomprising: determining a first gene group expression level for thefirst gene group, and determining a second gene group expression levelfor the second gene group; determining a first visual characteristic fora first graphical user interface (GUI) element using the first genegroup expression level; determining a second visual characteristic for asecond GUI element using the second gene group expression level;generating a personalized GUI personalized to the subject, the GUIcomprising: a first GUI portion associated with cancer malignancy andcontaining the first GUI element having the first visual characteristic,and a second GUI portion associated with cancer microenvironment andcontaining the second GUI element having the second visualcharacteristic; and presenting the generated personalized GUI to a user.

In some embodiments, determining the first visual characteristic for thefirst GUI element comprises determining size of the first GUI elementusing the first gene group expression level; and determining the secondvisual characteristic for the second GUI element comprises determiningsize of the second GUI element using the second gene group expressionlevel. In some embodiments, determining the first visual characteristicfor the first GUI element comprises determining color and/or pattern ofthe first GUI element using the first gene group expression level; anddetermining the second visual characteristic for the second GUI elementcomprises determining color and/or pattern of the second GUI elementusing the second gene group expression level. In some embodiments,determining the first visual characteristic for the first GUI elementcomprises determining shape of the first GUI element using the firstgene group expression level; and determining the second visualcharacteristic for the second GUI element comprises determining shape ofthe second GUI element using the second gene group expression level. Incertain embodiments, in response to a user selection of the first GUIelement, the GUI is configured to present information about at least oneadditional gene group associated with cancer malignancy. In certainembodiments, in response to a user selection of the second GUI element,the GUI is configured to present information about at least oneadditional gene group associated with cancer microenvironment.

In some embodiments, generating the personalized GUI comprisesgenerating the GUI comprising: a first portion associated with cancermalignancy and containing the first GUI element; and a second portionassociated with cancer microenvironment and containing the second GUIelement, wherein the second portion is different from the first portion.

In some embodiments, the first portion comprises a first plurality ofGUI elements including a GUI element for each of the gene groupsassociated with cancer malignancy, wherein the first plurality of GUIelements comprises the first GUI element; and the second portioncomprises a second plurality of GUI elements including a GUI element foreach of the gene groups associated with cancer microenvironment, whereinthe second plurality of GUI elements comprises the second GUI element.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject having a particulartype of cancer; determining a molecular-functional (MF) profile for thesubject at least in part by: determining, using the RNA expression dataand reference RNA expression data, a gene group expression level foreach gene group in a first set of gene groups associated with cancermalignancy and consisting of the tumor properties group; anddetermining, using the RNA expression data and the reference RNAexpression data, a gene group expression level for each gene group in asecond set of gene groups associated with cancer microenvironment andconsisting of the tumor-promoting immune microenvironment group, theanti-tumor immune microenvironment group, the angiogenesis group, andthe fibroblasts group; and accessing information specifying multiple MFprofile clusters for the particular cancer type; identifying, from amongthe multiple MF profile clusters, an MF profile cluster with which toassociate the MF profile for the subject, the MF profile clusterscomprising: a first MF profile cluster associated with inflamed andvascularized biological samples and/or inflamed and fibroblast-enrichedbiological samples, a second MF profile cluster associated with inflamedand non-vascularized biological samples and/or inflamed andnon-fibroblast-enriched biological samples, a third MF profile clusterassociated with non-inflamed and vascularized biological samples and/ornon-inflamed and fibroblast-enriched biological samples, and a fourth MFprofile cluster associated with non-inflamed and non-vascularizedbiological samples and/or non-inflamed and non-fibroblast-enrichedbiological sample, wherein the MF profile clusters were generated by:determining a plurality of MF profiles for a respective plurality ofsubjects using the reference RNA expression data and RNA expression datafrom biological samples obtained from the plurality of subjects, each ofthe plurality of MF profiles containing a gene group expression levelfor each gene group in the set of gene groups; and clustering theplurality of MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining RNA expressiondata and/or whole exome sequencing (WES) data for a biological samplefrom a subject having a particular type of cancer; determining amolecular-functional (MF) profile for the subject at least in part by:determining, using the RNA expression data and reference RNA expressiondata, a gene group expression level for each gene group in a first setof gene groups associated with cancer malignancy and consisting of thetumor properties group; and determining, using the RNA expression dataand the reference RNA expression data, a gene group expression level foreach gene group in a second set of gene groups associated with cancermicroenvironment and consisting of the tumor-promoting immunemicroenvironment group, the anti-tumor immune microenvironment group,the angiogenesis group, and the fibroblasts group; and accessinginformation specifying multiple MF profile clusters for the particularcancer type; identifying, from among the multiple MF profile clusters,an MF profile cluster with which to associate the MF profile for thesubject, the MF profile clusters comprising: a first MF profile clusterassociated with inflamed and vascularized biological samples and/orinflamed and fibroblast-enriched biological samples, a second MF profilecluster associated with inflamed and non-vascularized biological samplesand/or inflamed and non-fibroblast-enriched biological samples, a thirdMF profile cluster associated with non-inflamed and vascularizedbiological samples and/or non-inflamed and fibroblast-enrichedbiological samples, and a fourth MF profile cluster associated withnon-inflamed and non-vascularized biological samples and/or non-inflamedand non-fibroblast-enriched biological sample, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using the reference RNA expression dataand RNA expression data from biological samples obtained from theplurality of subjects, each of the plurality of MF profiles containing agene group expression level for each gene group in the set of genegroups; and clustering the plurality of MF profiles to obtain the MFprofile clusters.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject having a particulartype of cancer; determining a molecular-functional (MF) profile for thesubject at least in part by: determining, using the RNA expression dataand reference RNA expression data, a gene group expression level foreach gene group in a first set of gene groups associated with cancermalignancy and consisting of the tumor properties group; anddetermining, using the RNA expression data and the reference RNAexpression data, a gene group expression level for each gene group in asecond set of gene groups associated with cancer microenvironment andconsisting of the tumor-promoting immune microenvironment group, theanti-tumor immune microenvironment group, the angiogenesis group, andthe fibroblasts group; and accessing information specifying multiple MFprofile clusters for the particular cancer type; identifying, from amongthe multiple MF profile clusters, an MF profile cluster with which toassociate the MF profile for the subject, the MF profile clusterscomprising: a first MF profile cluster associated with inflamed andvascularized biological samples and/or inflamed and fibroblast-enrichedbiological samples, a second MF profile cluster associated with inflamedand non-vascularized biological samples and/or inflamed andnon-fibroblast-enriched biological samples, a third MF profile clusterassociated with non-inflamed and vascularized biological samples and/ornon-inflamed and fibroblast-enriched biological samples, and a fourth MFprofile cluster associated with non-inflamed and non-vascularizedbiological samples and/or non-inflamed and non-fibroblast-enrichedbiological sample, wherein the MF profile clusters were generated by:determining a plurality of MF profiles for a respective plurality ofsubjects using the reference RNA expression data and RNA expression datafrom biological samples obtained from the plurality of subjects, each ofthe plurality of MF profiles containing a gene group expression levelfor each gene group in the set of gene groups; and clustering theplurality of MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject having a particulartype of cancer; determining a molecular-functional (MF) profile for thesubject at least in part by: determining, using the RNA expression dataand reference RNA expression data, a gene group expression level foreach gene group in a first set of gene groups associated with cancermalignancy and consisting of the proliferation rate group, thePI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, thereceptor tyrosine kinases expression group, the tumor suppressors group,the metastasis signature group, the anti-metastatic factors group, andthe mutation status group; and determining, using the RNA expressiondata and the reference RNA expression data, a gene group expressionlevel for each gene group in a second set of gene groups associated withcancer microenvironment and consisting of the antigen presentationgroup, the cytotoxic T and NK cells group, the B cells group, theanti-tumor microenvironment group, the checkpoint inhibition group, theTreg group, the MDSC group, the granulocytes group, the cancerassociated fibroblasts group, the angiogenesis group, and thetumor-promotive immune group; and accessing information specifyingmultiple MF profile clusters for the particular cancer type;identifying, from among the multiple MF profile clusters, an MF profilecluster with which to associate the MF profile for the subject, the MFprofile clusters comprising: a first MF profile cluster associated withinflamed and vascularized biological samples and/or inflamed andfibroblast-enriched biological samples, a second MF profile clusterassociated with inflamed and non-vascularized biological samples and/orinflamed and non-fibroblast-enriched biological samples, a third MFprofile cluster associated with non-inflamed and vascularized biologicalsamples and/or non-inflamed and fibroblast-enriched biological samples,and a fourth MF profile cluster associated with non-inflamed andnon-vascularized biological samples and/or non-inflamed andnon-fibroblast-enriched biological samples, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using the reference RNA expression dataand RNA expression data from biological samples obtained from theplurality of subjects, each of the plurality of MF profiles containing agene group expression level for each gene group in the set of genegroups; and clustering the plurality of MF profiles to obtain the MFprofile clusters.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining obtaining RNAexpression data and/or whole exome sequencing (WES) data for abiological sample from a subject having a particular type of cancer;determining a molecular-functional (MF) profile for the subject at leastin part by: determining, using the RNA expression data and reference RNAexpression data, a gene group expression level for each gene group in afirst set of gene groups associated with cancer malignancy andconsisting of the proliferation rate group, the PI3K/AKT/mTOR signalinggroup, the RAS/RAF/MEK signaling group, the receptor tyrosine kinasesexpression group, the tumor suppressors group, the metastasis signaturegroup, the anti-metastatic factors group, and the mutation status group;and determining, using the RNA expression data and the reference RNAexpression data, a gene group expression level for each gene group in asecond set of gene groups associated with cancer microenvironment andconsisting of the antigen presentation group, the cytotoxic T and NKcells group, the B cells group, the anti-tumor microenvironment group,the checkpoint inhibition group, the Treg group, the MDSC group, thegranulocytes group, the cancer associated fibroblasts group, theangiogenesis group, and the tumor-promotive immune group; and accessinginformation specifying multiple MF profile clusters for the particularcancer type; identifying, from among the multiple MF profile clusters,an MF profile cluster with which to associate the MF profile for thesubject, the MF profile clusters comprising: a first MF profile clusterassociated with inflamed and vascularized biological samples and/orinflamed and fibroblast-enriched biological samples, a second MF profilecluster associated with inflamed and non-vascularized biological samplesand/or inflamed and non-fibroblast-enriched biological samples, a thirdMF profile cluster associated with non-inflamed and vascularizedbiological samples and/or non-inflamed and fibroblast-enrichedbiological samples, and a fourth MF profile cluster associated withnon-inflamed and non-vascularized biological samples and/or non-inflamedand non-fibroblast-enriched biological samples, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using the reference RNA expression dataand RNA expression data from biological samples obtained from theplurality of subjects, each of the plurality of MF profiles containing agene group expression level for each gene group in the set of genegroups; and clustering the plurality of MF profiles to obtain the MFprofile clusters.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject having a particulartype of cancer; determining a molecular-functional (MF) profile for thesubject at least in part by: determining, using the RNA expression dataand reference RNA expression data, a gene group expression level foreach gene group in a first set of gene groups associated with cancermalignancy and consisting of the proliferation rate group, thePI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, thereceptor tyrosine kinases expression group, the tumor suppressors group,the metastasis signature group, the anti-metastatic factors group, andthe mutation status group; and determining, using the RNA expressiondata and the reference RNA expression data, a gene group expressionlevel for each gene group in a second set of gene groups associated withcancer microenvironment and consisting of the antigen presentationgroup, the cytotoxic T and NK cells group, the B cells group, theanti-tumor microenvironment group, the checkpoint inhibition group, theTreg group, the MDSC group, the granulocytes group, the cancerassociated fibroblasts group, the angiogenesis group, and thetumor-promotive immune group; and accessing information specifyingmultiple MF profile clusters for the particular cancer type;identifying, from among the multiple MF profile clusters, an MF profilecluster with which to associate the MF profile for the subject, the MFprofile clusters comprising: a first MF profile cluster associated withinflamed and vascularized biological samples and/or inflamed andfibroblast-enriched biological samples, a second MF profile clusterassociated with inflamed and non-vascularized biological samples and/orinflamed and non-fibroblast-enriched biological samples, a third MFprofile cluster associated with non-inflamed and vascularized biologicalsamples and/or non-inflamed and fibroblast-enriched biological samples,and a fourth MF profile cluster associated with non-inflamed andnon-vascularized biological samples and/or non-inflamed andnon-fibroblast-enriched biological samples, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using the reference RNA expression dataand RNA expression data from biological samples obtained from theplurality of subjects, each of the plurality of MF profiles containing agene group expression level for each gene group in the set of genegroups; and clustering the plurality of MF profiles to obtain the MFprofile clusters.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject having a particulartype of cancer; determining a molecular-functional (MF) profile for thesubject at least in part by: determining, using the RNA expression dataand reference RNA expression data, a gene group expression level foreach gene group in a first set of gene groups associated with cancermalignancy and consisting of the proliferation rate group, thePI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, thereceptor tyrosine kinases expression group, the growth factors group,the tumor suppressors group, the metastasis signature group, theanti-metastatic factors group, and the mutation status group; anddetermining, using the RNA expression data and the reference RNAexpression data, a gene group expression level for each gene group in asecond set of gene groups associated with cancer microenvironment andconsisting of the MHCI group, the MHCII group, the coactivationmolecules group, the effector cells group, the NK cells group, the Tcell traffic group, the T cells group, the B cells group, the M1signatures group, the Th1 signature group, the antitumor cytokinesgroup, the checkpoint inhibition group, the Treg group, the MDSC group,the granulocytes group, the M2 signature group, the Th2 signature group,the protumor cytokines group, the cancer associated fibroblasts group,the angiogenesis group, and the complement inhibition group; andaccessing information specifying multiple MF profile clusters for theparticular cancer type; identifying, from among the multiple MF profileclusters, an MF profile cluster with which to associate the MF profilefor the subject, the MF profile clusters comprising: a first MF profilecluster associated with inflamed and vascularized biological samplesand/or inflamed and fibroblast-enriched biological samples, a second MFprofile cluster associated with inflamed and non-vascularized biologicalsamples and/or inflamed and non-fibroblast-enriched biological samples,a third MF profile cluster associated with non-inflamed and vascularizedbiological samples and/or non-inflamed and fibroblast-enrichedbiological samples, and a fourth MF profile cluster associated withnon-inflamed and non-vascularized biological samples and/or non-inflamedand non-fibroblast-enriched biological samples, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using the reference RNA expression dataand RNA expression data from biological samples obtained from theplurality of subjects, each of the plurality of MF profiles containing agene group expression level for each gene group in the set of genegroups; and clustering the plurality of MF profiles to obtain the MFprofile clusters.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining RNA expressiondata and/or whole exome sequencing (WES) data for a biological samplefrom a subject having a particular type of cancer; determining amolecular-functional (MF) profile for the subject at least in part by:determining, using the RNA expression data and reference RNA expressiondata, a gene group expression level for each gene group in a first setof gene groups associated with cancer malignancy and consisting of theproliferation rate group, the PI3K/AKT/mTOR signaling group, theRAS/RAF/MEK signaling group, the receptor tyrosine kinases expressiongroup, the growth factors group, the tumor suppressors group, themetastasis signature group, the anti-metastatic factors group, and themutation status group; and determining, using the RNA expression dataand the reference RNA expression data, a gene group expression level foreach gene group in a second set of gene groups associated with cancermicroenvironment and consisting of the MHCI group, the MHCII group, thecoactivation molecules group, the effector cells group, the NK cellsgroup, the T cell traffic group, the T cells group, the B cells group,the M1 signatures group, the Th1 signature group, the antitumorcytokines group, the checkpoint inhibition group, the Treg group, theMDSC group, the granulocytes group, the M2 signature group, the Th2signature group, the protumor cytokines group, the cancer associatedfibroblasts group, the angiogenesis group, and the complement inhibitiongroup; and accessing information specifying multiple MF profile clustersfor the particular cancer type; identifying, from among the multiple MFprofile clusters, an MF profile cluster with which to associate the MFprofile for the subject, the MF profile clusters comprising: a first MFprofile cluster associated with inflamed and vascularized biologicalsamples and/or inflamed and fibroblast-enriched biological samples, asecond MF profile cluster associated with inflamed and non-vascularizedbiological samples and/or inflamed and non-fibroblast-enrichedbiological samples, a third MF profile cluster associated withnon-inflamed and vascularized biological samples and/or non-inflamed andfibroblast-enriched biological samples, and a fourth MF profile clusterassociated with non-inflamed and non-vascularized biological samplesand/or non-inflamed and non-fibroblast-enriched biological samples,wherein the MF profile clusters were generated by: determining aplurality of MF profiles for a respective plurality of subjects usingthe reference RNA expression data and RNA expression data frombiological samples obtained from the plurality of subjects, each of theplurality of MF profiles containing a gene group expression level foreach gene group in the set of gene groups; and clustering the pluralityof MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject having a particulartype of cancer; determining a molecular-functional (MF) profile for thesubject at least in part by: determining, using the RNA expression dataand reference RNA expression data, a gene group expression level foreach gene group in a first set of gene groups associated with cancermalignancy and consisting of the proliferation rate group, thePI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, thereceptor tyrosine kinases expression group, the growth factors group,the tumor suppressors group, the metastasis signature group, theanti-metastatic factors group, and the mutation status group; anddetermining, using the RNA expression data and the reference RNAexpression data, a gene group expression level for each gene group in asecond set of gene groups associated with cancer microenvironment andconsisting of the MHCI group, the MHCII group, the coactivationmolecules group, the effector cells group, the NK cells group, the Tcell traffic group, the T cells group, the B cells group, the M1signatures group, the Th1 signature group, the antitumor cytokinesgroup, the checkpoint inhibition group, the Treg group, the MDSC group,the granulocytes group, the M2 signature group, the Th2 signature group,the protumor cytokines group, the cancer associated fibroblasts group,the angiogenesis group, and the complement inhibition group; andaccessing information specifying multiple MF profile clusters for theparticular cancer type; identifying, from among the multiple MF profileclusters, an MF profile cluster with which to associate the MF profilefor the subject, the MF profile clusters comprising: a first MF profilecluster associated with inflamed and vascularized biological samplesand/or inflamed and fibroblast-enriched biological samples, a second MFprofile cluster associated with inflamed and non-vascularized biologicalsamples and/or inflamed and non-fibroblast-enriched biological samples,a third MF profile cluster associated with non-inflamed and vascularizedbiological samples and/or non-inflamed and fibroblast-enrichedbiological samples, and a fourth MF profile cluster associated withnon-inflamed and non-vascularized biological samples and/or non-inflamedand non-fibroblast-enriched biological samples, wherein the MF profileclusters were generated by: determining a plurality of MF profiles for arespective plurality of subjects using the reference RNA expression dataand RNA expression data from biological samples obtained from theplurality of subjects, each of the plurality of MF profiles containing agene group expression level for each gene group in the set of genegroups; and clustering the plurality of MF profiles to obtain the MFprofile clusters.

In some embodiments, the gene groups associated with cancer malignancycomprise at least three genes from each of the following groups: theproliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1,AURKA, AURKB, CDK4, CDK6, PRC1, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2,and MCM6; the PI3K/AKT/mTOR signaling group: PIK3CA, PIK3CB, PIK3CG,PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, and AKT3; the RAS/RAF/MEKsignaling group: BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, and MKNK2; thereceptor tyrosine kinases expression group: ALK, AXL, KIT, EGFR, ERBB2,FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA,and PDGFRB; the growth factors group: NGF, CSF3, CSF2, FGF7, IGF1, IGF2,IL7, and FGF2; the tumor suppressors group: TP53, SIK1, PTEN, DCN, MTAP,AIM2, and RB1; the metastasis signature group: ESRP1, CTSL, HOXA1,SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA, and HPSE; the anti-metastaticfactors group: KISS1, ADGRG1, BRMS1, TCF21, CDH1, PCDH10, NCAM1, andMITF; and the mutation status group: APC, ARID1A, ATM, ATRX, BAP1, BRAF,BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3,HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS,PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, andVHL.

In some embodiments, determining the MF portrait comprises: determiningthe gene group expression level for the proliferation rate group usingthe gene expression level obtained from the RNA sequence data for atleast three genes in the proliferation rate group; determining the genegroup expression level for the PI3K/AKT/mTOR signaling group using thegene expression level obtained from the RNA sequence data for at leastthree genes in the PI3K/AKT/mTOR signaling group; determining the genegroup expression level for the RAS/RAF/MEK signaling group using thegene expression level obtained from the RNA sequence data for at leastthree genes in the RAS/RAF/MEK signaling group; determining the genegroup expression level for the receptor tyrosine kinases expressiongroup using the gene expression level obtained from the RNA sequencedata for at least three genes in the receptor tyrosine kinasesexpression group; determining the gene group expression level for thegrowth factors group using the gene expression level obtained from theRNA sequence data for at least three genes in the growth factors group;determining the gene group expression level for the tumor suppressorsgroup using the gene expression level obtained from the RNA sequencedata for at least three genes in the tumor suppressors group;determining the gene group expression level for the metastasis signaturegroup using the gene expression level obtained from the RNA sequencedata for at least three genes in the metastasis signature group;determining the gene group expression level for the anti-metastaticfactors group using the gene expression level obtained from the RNAsequence data for at least three genes in the anti-metastatic factorsgroup; and determining the gene group expression level for the mutationstatus group using the gene expression level obtained from the RNAsequence data for at least three genes in the mutation status group.

In some embodiments, determining the MF profile for the subjectcomprises: determining a first gene group expression level for a firstgene group of the first set of gene groups associated with cancermalignancy using a gene set enrichment analysis (GSEA) technique; anddetermining a second gene group expression level for a second gene groupof the second set of gene groups associated with cancer microenvironmentusing the gene set enrichment analysis (GSEA) technique.

In some embodiments, determining the MF profile for the subjectcomprises: determining a first gene group expression level for a firstgene group of the first set of gene groups associated with cancermalignancy using a mutation count technique; and determining a secondgene group expression level for a second gene group of the second set ofgene groups associated with cancer microenvironment using the mutationcount technique.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining first RNA expression data and/or first whole exomesequencing (WES) data from biological samples from a plurality ofsubjects; determining a respective plurality of molecular-functional(MF) profiles for the plurality of subjects at least in part by, foreach of the plurality of subjects, determining, using the first RNAexpression data, a respective gene group expression level for each groupin a set of gene groups, the set of gene groups comprising gene groupsassociated with cancer malignancy and different gene groups associatedwith cancer microenvironment; clustering the plurality of MF profiles toobtain MF profile clusters including: a first MF profile clusterassociated with inflamed and vascularized biological samples and/orinflamed and fibroblast-enriched biological samples, a second MF profilecluster associated with inflamed and non-vascularized biological samplesand/or inflamed and non-fibroblast-enriched biological samples, a thirdMF profile cluster associated with non-inflamed and vascularizedbiological samples and/or non-inflamed and fibroblast-enrichedbiological samples, and a fourth MF profile cluster associated withnon-inflamed and non-vascularized biological samples and/or non-inflamedand non-fibroblast-enriched biological samples; obtaining second RNAexpression data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the second RNA expression data, a gene groupexpression level for each group in the set of gene groups; andidentifying, from among the MF profile clusters, a particular MF profilecluster with which to associate the MF profile for the subject.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining first RNAexpression data and/or first whole exome sequencing (WES) data frombiological samples from a plurality of subjects; determining arespective plurality of molecular-functional (MF) profiles for theplurality of subjects at least in part by, for each of the plurality ofsubjects, determining, using the first RNA expression data, a respectivegene group expression level for each group in a set of gene groups, theset of gene groups comprising gene groups associated with cancermalignancy and different gene groups associated with cancermicroenvironment; clustering the plurality of MF profiles to obtain MFprofile clusters including: a first MF profile cluster associated withinflamed and vascularized biological samples and/or inflamed andfibroblast-enriched biological samples, a second MF profile clusterassociated with inflamed and non-vascularized biological samples and/orinflamed and non-fibroblast-enriched biological samples, a third MFprofile cluster associated with non-inflamed and vascularized biologicalsamples and/or non-inflamed and fibroblast-enriched biological samples,and a fourth MF profile cluster associated with non-inflamed andnon-vascularized biological samples and/or non-inflamed andnon-fibroblast-enriched biological samples; obtaining second RNAexpression data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the second RNA expression data, a gene groupexpression level for each group in the set of gene groups; andidentifying, from among the MF profile clusters, a particular MF profilecluster with which to associate the MF profile for the subject.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining first RNA expression data and/or first whole exomesequencing (WES) data from biological samples from a plurality ofsubjects; determining a respective plurality of molecular-functional(MF) profiles for the plurality of subjects at least in part by, foreach of the plurality of subjects, determining, using the first RNAexpression data, a respective gene group expression level for each groupin a set of gene groups, the set of gene groups comprising gene groupsassociated with cancer malignancy and different gene groups associatedwith cancer microenvironment; clustering the plurality of MF profiles toobtain MF profile clusters including: a first MF profile clusterassociated with inflamed and vascularized biological samples and/orinflamed and fibroblast-enriched biological samples, a second MF profilecluster associated with inflamed and non-vascularized biological samplesand/or inflamed and non-fibroblast-enriched biological samples, a thirdMF profile cluster associated with non-inflamed and vascularizedbiological samples and/or non-inflamed and fibroblast-enrichedbiological samples, and a fourth MF profile cluster associated withnon-inflamed and non-vascularized biological samples and/or non-inflamedand non-fibroblast-enriched biological samples; obtaining second RNAexpression data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the second RNA expression data, a gene groupexpression level for each group in the set of gene groups; andidentifying, from among the MF profile clusters, a particular MF profilecluster with which to associate the MF profile for the subject.

In some embodiments, the first portion comprises a first plurality ofGUI elements representing a respective plurality of gene groupsassociated with cancer malignancy; and the second portion comprises asecond plurality of GUI elements representing a respective plurality ofgene groups associated with cancer microenvironment.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups comprising: first genegroups associated with cancer malignancy consisting of the tumorproperties group; and second gene groups associated with cancermicroenvironment consisting of the tumor-promoting immunemicroenvironment group, the anti-tumor immune microenvironment group,the angiogenesis group, and the fibroblasts group, determining a firstset of visual characteristics for a first plurality of graphical userinterface (GUI) elements using the gene group expression levelsdetermined for the first gene groups; determining a second set of visualcharacteristics for a second plurality of GUI elements using the genegroup expression levels determined for the second gene groups;generating a personalized GUI personalized to the subject, thegenerating comprising: generating a first GUI portion associated withcancer malignancy and containing the first plurality of GUI elementshaving the determined first set of visual characteristics; andgenerating a second GUI portion associated with cancer microenvironmentand containing the second plurality of GUI elements having thedetermined second set of visual characteristics; and presenting thegenerated personalized GUI to a user.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining RNA expressiondata and/or whole exome sequencing (WES) data for a biological samplefrom a subject; determining a molecular-functional (MF) profile for thesubject at least in part by determining, using the RNA expression data,a gene group expression level for each gene group in a set of genegroups comprising: first gene groups associated with cancer malignancyconsisting of the tumor properties group; and second gene groupsassociated with cancer microenvironment consisting of thetumor-promoting immune microenvironment group, the anti-tumor immunemicroenvironment group, the angiogenesis group, and the fibroblastsgroup, determining a first set of visual characteristics for a firstplurality of graphical user interface (GUI) elements using the genegroup expression levels determined for the first gene groups;determining a second set of visual characteristics for a secondplurality of GUI elements using the gene group expression levelsdetermined for the second gene groups; generating a personalized GUIpersonalized to the subject, the generating comprising: generating afirst GUI portion associated with cancer malignancy and containing thefirst plurality of GUI elements having the determined first set ofvisual characteristics; and generating a second GUI portion associatedwith cancer microenvironment and containing the second plurality of GUIelements having the determined second set of visual characteristics; andpresenting the generated personalized GUI to a user.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups comprising: first genegroups associated with cancer malignancy consisting of the tumorproperties group; and second gene groups associated with cancermicroenvironment consisting of the tumor-promoting immunemicroenvironment group, the anti-tumor immune microenvironment group,the angiogenesis group, and the fibroblasts group, determining a firstset of visual characteristics for a first plurality of graphical userinterface (GUI) elements using the gene group expression levelsdetermined for the first gene groups; determining a second set of visualcharacteristics for a second plurality of GUI elements using the genegroup expression levels determined for the second gene groups;generating a personalized GUI personalized to the subject, thegenerating comprising: generating a first GUI portion associated withcancer malignancy and containing the first plurality of GUI elementshaving the determined first set of visual characteristics; andgenerating a second GUI portion associated with cancer microenvironmentand containing the second plurality of GUI elements having thedetermined second set of visual characteristics; and presenting thegenerated personalized GUI to a user.

In some embodiments, determining the first set of visual characteristicsfor the first plurality of GUI elements determining sizes for each ofthe first plurality of GUI elements using the gene expression levelsdetermined for the first gene groups; and determining the second set ofvisual characteristics for the first plurality of GUI elementsdetermining sizes for each of the second plurality of GUI elements usingthe gene expression levels determined for the second gene groups.

In some embodiments, determining the MF profile for the subjectcomprises determining the gene expression levels for each of the firstgene groups using a gene set enrichment analysis (GSEA) technique; anddetermining the MF profile for the subject comprises determining thegene expression levels for each of the second gene groups using the geneset enrichment analysis (GSEA) technique.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups comprising: first genegroups associated with cancer malignancy consisting of the proliferationrate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signalinggroup, the receptor tyrosine kinases expression group, the tumorsuppressors group, the metastasis signature group, the anti-metastaticfactors group, and the mutation status group; and second gene groupsassociated with cancer microenvironment consisting of the cancerassociated fibroblasts group, the angiogenesis group, the antigenpresentation group, the cytotoxic T and NK cells group, the B cellsgroup, the anti-tumor microenvironment group, the checkpoint inhibitiongroup, the Treg group, the MDSC group, the granulocytes group, and thetumor-promotive immune group; determining a first set of visualcharacteristics for a first plurality of graphical user interface (GUI)elements using the gene group expression levels determined for the firstgene groups; determining a second set of visual characteristics for asecond plurality of GUI elements using the gene group expression levelsdetermined for the second gene groups; generating a personalized GUIpersonalized to the subject, the generating comprising: generating afirst GUI portion associated with cancer malignancy and containing thefirst plurality of GUI elements having the determined first set ofvisual characteristics; and generating a second GUI portion associatedwith cancer microenvironment and containing the second plurality of GUIelements having the determined second set of visual characteristics; andpresenting the generated personalized GUI to a user.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining RNA expressiondata and/or whole exome sequencing (WES) data for a biological samplefrom a subject; determining a molecular-functional (MF) profile for thesubject at least in part by determining, using the RNA expression data,a gene group expression level for each gene group in a set of genegroups comprising: first gene groups associated with cancer malignancyconsisting of the proliferation rate group, the PI3K/AKT/mTOR signalinggroup, the RAS/RAF/MEK signaling group, the receptor tyrosine kinasesexpression group, the tumor suppressors group, the metastasis signaturegroup, the anti-metastatic factors group, and the mutation status group;and second gene groups associated with cancer microenvironmentconsisting of the cancer associated fibroblasts group, the angiogenesisgroup, the antigen presentation group, the cytotoxic T and NK cellsgroup, the B cells group, the anti-tumor microenvironment group, thecheckpoint inhibition group, the Treg group, the MDSC group, thegranulocytes group, and the tumor-promotive immune group; determining afirst set of visual characteristics for a first plurality of graphicaluser interface (GUI) elements using the gene group expression levelsdetermined for the first gene groups; determining a second set of visualcharacteristics for a second plurality of GUI elements using the genegroup expression levels determined for the second gene groups;generating a personalized GUI personalized to the subject, thegenerating comprising: generating a first GUI portion associated withcancer malignancy and containing the first plurality of GUI elementshaving the determined first set of visual characteristics; andgenerating a second GUI portion associated with cancer microenvironmentand containing the second plurality of GUI elements having thedetermined second set of visual characteristics; and presenting thegenerated personalized GUI to a user.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups comprising: first genegroups associated with cancer malignancy consisting of the proliferationrate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signalinggroup, the receptor tyrosine kinases expression group, the tumorsuppressors group, the metastasis signature group, the anti-metastaticfactors group, and the mutation status group; and second gene groupsassociated with cancer microenvironment consisting of the cancerassociated fibroblasts group, the angiogenesis group, the antigenpresentation group, the cytotoxic T and NK cells group, the B cellsgroup, the anti-tumor microenvironment group, the checkpoint inhibitiongroup, the Treg group, the MDSC group, the granulocytes group, and thetumor-promotive immune group; determining a first set of visualcharacteristics for a first plurality of graphical user interface (GUI)elements using the gene group expression levels determined for the firstgene groups; determining a second set of visual characteristics for asecond plurality of GUI elements using the gene group expression levelsdetermined for the second gene groups; generating a personalized GUIpersonalized to the subject, the generating comprising: generating afirst GUI portion associated with cancer malignancy and containing thefirst plurality of GUI elements having the determined first set ofvisual characteristics; and generating a second GUI portion associatedwith cancer microenvironment and containing the second plurality of GUIelements having the determined second set of visual characteristics; andpresenting the generated personalized GUI to a user.

In one aspect, provided herein is a system, comprising: at least onecomputer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups comprising: first genegroups associated with cancer malignancy consisting of the proliferationrate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signalinggroup, the receptor tyrosine kinases expression group, the growthfactors group, the tumor suppressors group, the metastasis signaturegroup, the anti-metastatic factors group, and the mutation status group;and second gene groups associated with cancer microenvironmentconsisting of the cancer associated fibroblasts group, the angiogenesisgroup, the MHCI group, the MHCII group, the coactivation moleculesgroup, the effector cells group, the NK cells group, the T cell trafficgroup, the T cells group, the B cells group, the M1 signatures group,the Th1 signature group, the antitumor cytokines group, the checkpointinhibition group, the Treg group, the MDSC group, the granulocytesgroup, the M2 signature group, the Th2 signature group, the protumorcytokines group, and the complement inhibition group; determining afirst set of visual characteristics for a first plurality of graphicaluser interface (GUI) elements using the gene group expression levelsdetermined for the first gene groups; determining a second set of visualcharacteristics for a second plurality of GUI elements using the genegroup expression levels determined for the second gene groups;generating a personalized GUI personalized to the subject, thegenerating comprising: generating a first GUI portion associated withcancer malignancy and containing the first plurality of GUI elementshaving the determined first set of visual characteristics; andgenerating a second GUI portion associated with cancer microenvironmentand containing the second plurality of GUI elements having thedetermined second set of visual characteristics; and presenting thegenerated personalized GUI to a user.

In one aspect, provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining RNA expressiondata and/or whole exome sequencing (WES) data for a biological samplefrom a subject; determining a molecular-functional (MF) profile for thesubject at least in part by determining, using the RNA expression data,a gene group expression level for each gene group in a set of genegroups comprising: first gene groups associated with cancer malignancyconsisting of the proliferation rate group, the PI3K/AKT/mTOR signalinggroup, the RAS/RAF/MEK signaling group, the receptor tyrosine kinasesexpression group, the growth factors group, the tumor suppressors group,the metastasis signature group, the anti-metastatic factors group, andthe mutation status group; and second gene groups associated with cancermicroenvironment consisting of the cancer associated fibroblasts group,the angiogenesis group, the MHCI group, the MHCII group, thecoactivation molecules group, the effector cells group, the NK cellsgroup, the T cell traffic group, the T cells group, the B cells group,the M1 signatures group, the Th1 signature group, the antitumorcytokines group, the checkpoint inhibition group, the Treg group, theMDSC group, the granulocytes group, the M2 signature group, the Th2signature group, the protumor cytokines group, and the complementinhibition group; determining a first set of visual characteristics fora first plurality of graphical user interface (GUI) elements using thegene group expression levels determined for the first gene groups;determining a second set of visual characteristics for a secondplurality of GUI elements using the gene group expression levelsdetermined for the second gene groups; generating a personalized GUIpersonalized to the subject, the generating comprising: generating afirst GUI portion associated with cancer malignancy and containing thefirst plurality of GUI elements having the determined first set ofvisual characteristics; and generating a second GUI portion associatedwith cancer microenvironment and containing the second plurality of GUIelements having the determined second set of visual characteristics; andpresenting the generated personalized GUI to a user.

In one aspect, provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining RNA expression data and/or whole exome sequencing(WES) data for a biological sample from a subject; determining amolecular-functional (MF) profile for the subject at least in part bydetermining, using the RNA expression data, a gene group expressionlevel for each gene group in a set of gene groups comprising: first genegroups associated with cancer malignancy consisting of the proliferationrate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signalinggroup, the receptor tyrosine kinases expression group, the growthfactors group, the tumor suppressors group, the metastasis signaturegroup, the anti-metastatic factors group, and the mutation status group;and second gene groups associated with cancer microenvironmentconsisting of the cancer associated fibroblasts group, the angiogenesisgroup, the MHCI group, the MHCII group, the coactivation moleculesgroup, the effector cells group, the NK cells group, the T cell trafficgroup, the T cells group, the B cells group, the M1 signatures group,the Th1 signature group, the antitumor cytokines group, the checkpointinhibition group, the Treg group, the MDSC group, the granulocytesgroup, the M2 signature group, the Th2 signature group, the protumorcytokines group, and the complement inhibition group; determining afirst set of visual characteristics for a first plurality of graphicaluser interface (GUI) elements using the gene group expression levelsdetermined for the first gene groups; determining a second set of visualcharacteristics for a second plurality of GUI elements using the genegroup expression levels determined for the second gene groups;generating a personalized GUI personalized to the subject, thegenerating comprising: generating a first GUI portion associated withcancer malignancy and containing the first plurality of GUI elementshaving the determined first set of visual characteristics; andgenerating a second GUI portion associated with cancer microenvironmentand containing the second plurality of GUI elements having thedetermined second set of visual characteristics; and presenting thegenerated personalized GUI to a user.

EQUIVALENTS AND SCOPE

The terms “program” or “software” are used herein in a generic sense torefer to any type of computer code or set of processor-executableinstructions that can be employed to program a computer or otherprocessor (physical or virtual) to implement various aspects ofembodiments as discussed above. Additionally, according to one aspect,one or more computer programs that when executed perform methods of thetechnology described herein need not reside on a single computer orprocessor, but may be distributed in a modular fashion among differentcomputers or processors to implement various aspects of the technologydescribed herein.

Processor-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Generally,program modules include routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. Typically, the functionality of the program modulesmay be combined or distributed.

Also, data structures may be stored in one or more non-transitorycomputer-readable storage media in any suitable form. For simplicity ofillustration, data structures may be shown to have fields that arerelated through location in the data structure. Such relationships maylikewise be achieved by assigning storage for the fields with locationsin a non-transitory computer-readable medium that convey relationshipbetween the fields. However, any suitable mechanism may be used toestablish relationships among information in fields of a data structure,including through the use of pointers, tags or other mechanisms thatestablish relationships among data elements.

Various inventive concepts may be embodied as one or more processes, ofwhich examples have been provided. The acts performed as part of eachprocess may be ordered in any suitable way. Thus, embodiments may beconstructed in which acts are performed in an order different thanillustrated, which may include performing some acts simultaneously, eventhough shown as sequential acts in illustrative embodiments.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, forexample, “at least one of A and B” (or, equivalently, “at least one of Aor B,” or, equivalently “at least one of A and/or B”) can refer, in oneembodiment, to at least one, optionally including more than one, A, withno B present (and optionally including elements other than B); inanother embodiment, to at least one, optionally including more than one,B, with no A present (and optionally including elements other than A);in yet another embodiment, to at least one, optionally including morethan one, A, and at least one, optionally including more than one, B(and optionally including other elements); etc.

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Other elements may optionally be presentother than the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.Thus, as an example, a reference to “A and/or B”, when used inconjunction with open-ended language such as “comprising” can refer, inone embodiment, to A only (optionally including elements other than B);in another embodiment, to B only (optionally including elements otherthan A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

In the claims articles such as “a,” “an,” and “the” may mean one or morethan one unless indicated to the contrary or otherwise evident from thecontext. Claims or descriptions that include “or” between one or moremembers of a group are considered satisfied if one, more than one, orall of the group members are present in, employed in, or otherwiserelevant to a given product or process unless indicated to the contraryor otherwise evident from the context. The disclosure includesembodiments in which exactly one member of the group is present in,employed in, or otherwise relevant to a given product or process. Thedisclosure includes embodiments in which more than one, or all of thegroup members are present in, employed in, or otherwise relevant to agiven product or process.

Furthermore, the described methods and systems encompass all variations,combinations, and permutations in which one or more limitations,elements, clauses, and descriptive terms from one or more of the listedclaims is introduced into another claim. For example, any claim that isdependent on another claim can be modified to include one or morelimitations found in any other claim that is dependent on the same baseclaim. Where elements are presented as lists, e.g., in Markush groupformat, each subgroup of the elements is also disclosed, and anyelement(s) can be removed from the group. It should it be understoodthat, in general, where the systems and methods described herein (oraspects thereof) are referred to as comprising particular elementsand/or features, certain embodiments of the systems and methods oraspects of the same consist, or consist essentially of, such elementsand/or features. For purposes of simplicity, those embodiments have notbeen specifically set forth in haec verba herein.

It is also noted that the terms “including,” “comprising,” “having,”“containing”, “involving”, are intended to be open and permits theinclusion of additional elements or steps. Where ranges are given,endpoints are included. Furthermore, unless otherwise indicated orotherwise evident from the context and understanding of one of ordinaryskill in the art, values that are expressed as ranges can assume anyspecific value or sub-range within the stated ranges in differentembodiments of the described systems and methods, to the tenth of theunit of the lower limit of the range, unless the context clearlydictates otherwise.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed. Such terms areused merely as labels to distinguish one claim element having a certainname from another element having a same name (but for use of the ordinalterm).

Additionally, as used herein the terms “patient” and “subject” may beused interchangeably. Such terms may include, but are not limited to,human subjects or patients. Such terms may also include non-humanprimates or other animals.

This application refers to various issued patents, published patentapplications, journal articles, and other publications, all of which areincorporated herein by reference. If there is a conflict between any ofthe incorporated references and the instant specification, thespecification shall control. In addition, any particular embodiment ofthe present disclosure that fall within the prior art may be explicitlyexcluded from any one or more of the claims. Because such embodimentsare deemed to be known to one of ordinary skill in the art, they may beexcluded even if the exclusion is not set forth explicitly herein. Anyparticular embodiment of the systems and methods described herein can beexcluded from any claim, for any reason, whether or not related to theexistence of prior art.

Those skilled in the art will recognize or be able to ascertain using nomore than routine experimentation many equivalents to the specificembodiments described herein. The scope of the present embodimentsdescribed herein is not intended to be limited to the above Description,but rather is as set forth in the appended claims. Those of ordinaryskill in the art will appreciate that various changes and modificationsto this description may be made without departing from the spirit orscope of the present disclosure, as defined in the following claims.

The invention claimed is:
 1. At least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one hardware processor,cause the at least one hardware processor to perform: obtaining RNAexpression data for a biological sample from a patient having, suspectedof having, or at risk of having cancer; determining, by processing theRNA expression data with software, gene group expression levels for arespective set of gene groups, the gene groups including a first set ofone or more gene groups associated with cancer malignancy and a secondset of one or more gene groups associated with cancer microenvironment;generating a graphical user interface (GUI) to assist a clinician inidentifying a combination therapy for the patient, the GUI having: afirst GUI portion containing a visualization for indicating one or moretherapies; and a second GUI portion containing a visualization of atleast some of the gene groups, the visualization including GUI elementscorresponding to the at least some of the gene groups, wherein one ormore of the GUI elements each correspond to a gene group, from among theat least some of the gene groups, comprising multiple genes and visualcharacteristics of the GUI elements are identified using the gene groupexpression levels; receiving first input indicating one or more of thegene groups visualized in the second GUI portion to target in thecombination therapy for the patient, wherein the first input indicates agene group comprising multiple genes; and updating, based at least inpart on the first input, the visualization in the first GUI portion toindicate a first combination of therapies including a first therapy anda second therapy and displaying the updated visualization, wherein thefirst combination of therapies is directed at regulating the one or moreof the gene groups.
 2. The at least one non-transitory computer-readablestorage medium of claim 1, wherein executing the processor-executableinstructions causes the at least one hardware processor to furtherperform updating, based at least in part on the first input, the secondGUI portion to visually emphasize one or more of the GUI elements toindicate gene groups associated with the first combination of therapiesand displaying the updated second GUI portion.
 3. The at least onenon-transitory computer-readable storage medium of claim 2, whereinupdating the second GUI portion to visually emphasize one or more of theGUI elements further comprises generating, in the second GUI portion,one or more markers proximate to the one or more GUI elements.
 4. The atleast one non-transitory computer-readable storage medium of claim 2,wherein updating the second GUI portion further comprises visuallyemphasizing one or more GUI elements corresponding to one or more genegroups associated with cancer malignancy targeted by the firstcombination of therapies.
 5. The at least one non-transitorycomputer-readable storage medium of claim 2, wherein updating the secondGUI portion further comprises visually emphasizing one or more GUIelements corresponding to one or more gene groups associated with cancermicroenvironment targeted by the first combination of therapies.
 6. Theat least one non-transitory computer-readable storage medium of claim 1,wherein generating the GUI further comprises: determining a first sizefor a first GUI element of the GUI elements using one or more geneexpression levels determined for a gene group corresponding to the firstGUI element; determining a second size for a second GUI element of theGUI elements using one or more expression levels determined for a genegroup corresponding to the second GUI element; determining a respectivecolor for each of the first GUI element and the second GUI element toobtain a first color and a second color based on whether thecorresponding gene group represents an anti-tumor process or a pro-tumorprocess; generating the first GUI element in the second GUI portion tohave the first size and the first color; and generating the second GUIelement in the second GUI portion to have the second size and the secondcolor.
 7. The at least one non-transitory computer-readable storagemedium of claim 1, wherein the GUI further has a third GUI portioncontaining a visualization of biomarkers, and the processor-executableinstructions that, when executed by at least one hardware processor,cause the at least one hardware processor to further perform: receivingsecond input indicating at least one of the biomarkers specified by theclinician to target in the combination therapy for the patient; andupdating, based at least in part on the second input, the first GUIportion to display a visualization of a second combination of therapiesincluding a third therapy, wherein the third therapy is directed atregulating the at least one of the biomarkers.
 8. The at least onenon-transitory computer-readable storage medium of claim 7, wherein thevisualization of biomarkers includes at least one biomarker associatedwith an immunotherapy.
 9. The at least one non-transitorycomputer-readable storage medium of claim 7, wherein the visualizationof biomarkers includes at least one biomarker associated with a targetedtherapy.
 10. A system comprising: at least one hardware processor; andat least one non-transitory computer-readable storage medium storingprocessor-executable instructions that, when executed by the at leastone hardware processor, cause the at least one hardware processor toperform: obtaining RNA expression data for a biological sample from apatient having, suspected of having, or at risk of having cancer;determining, by processing the RNA expression data with software, genegroup expression levels for a respective set of gene groups, the genegroups including a first set of one or more gene groups associated withcancer malignancy and a second set of one or more gene groups associatedwith cancer microenvironment; generating a graphical user interface(GUI) to assist a clinician in identifying a combination therapy for thepatient, the GUI having: a first GUI portion containing a visualizationfor indicating one or more therapies; and a second GUI portioncontaining a visualization of at least some of the gene groups, thevisualization including GUI elements corresponding to the at least someof the gene groups, wherein one or more of the GUI elements eachcorrespond to a gene group, from among the at least some of the genegroups, comprising multiple genes and visual characteristics of the GUIelements are identified using the gene group expression levels;receiving first input indicating one or more of the gene groupsvisualized in the second GUI portion to target in the combinationtherapy for the patient, wherein the first input indicates a gene groupcomprising multiple genes; and updating, based at least in part on thefirst input, the visualization in the first GUI portion to indicate afirst combination of therapies including a first therapy and a secondtherapy and displaying the updated visualization, wherein the firstcombination of therapies is directed at regulating the one or more ofthe gene groups.
 11. The system of claim 10, wherein executing theprocessor-executable instructions causes the at least one hardwareprocessor to further perform presenting, in the GUI, informationrelating to proposed effectiveness of the first combination oftherapies.
 12. The system of claim 10, wherein executing theprocessor-executable instructions causes the at least one hardwareprocessor to further perform presenting, in the GUI, informationrelating to potential adverse effects of the first combination oftherapies.
 13. The system of claim 10, wherein executing theprocessor-executable instructions causes the at least one hardwareprocessor to further perform presenting, in the GUI, informationrelating to published clinical trials associated with the firstcombination of therapies.
 14. The system of claim 10, wherein executingthe processor-executable instructions causes the at least one hardwareprocessor to further perform presenting, in the GUI, informationrelating to ongoing clinical trials associated with the firstcombination of therapies.
 15. The system of claim 10, wherein executingthe processor-executable instructions causes the at least one hardwareprocessor to further perform presenting, in the GUI, informationrelating to biological influence of the first combination of therapies.16. A method for assisting a clinician in designing combinationtherapies for treating patients having, suspected of having, or at riskof having cancer, the method comprising: obtaining RNA expression datafor a biological sample from a patient having, suspected of having, orat risk of having cancer; determining, by processing the RNA expressiondata with software, gene group expression levels for a respective set ofgene groups, the gene groups including a first set of one or more genegroups associated with cancer malignancy and a second set of one or moregene groups associated with cancer microenvironment; generating agraphical user interface (GUI) to assist the clinician in identifying acombination therapy for the patient, the GUI having: a first GUI portioncontaining a visualization for indicating one or more therapies; and asecond GUI portion containing a visualization of at least some of thegene groups, the visualization including GUI elements corresponding tothe at least some of the gene groups, wherein one or more of the GUIelements each correspond to a gene group, from among the at least someof the gene groups, comprising multiple genes and visual characteristicsof the GUI elements are identified using the gene group expressionlevels; receiving first input indicating one or more of the gene groupsvisualized in the second GUI portion to target in the combinationtherapy for the patient, wherein the first input indicates a gene groupcomprising multiple genes; and updating, based at least in part on thefirst input, the visualization in the first GUI portion to indicate afirst combination of therapies including a first therapy and a secondtherapy and displaying the updated visualization, wherein the firstcombination of therapies is directed at regulating the one or more ofthe gene groups.
 17. The method of claim 16, wherein the firstcombination of therapies includes at least one therapy selected from thegroup consisting of: chemotherapy, antibody drug conjugates, hormonaltherapy, viral therapy, genetic therapy, non-immune protein therapy,antiangiogenic agents, anti-cancer vaccines, radiotherapy, solublereceptor therapy, cell based therapy, adoptive T-cell therapy,immunotherapy, and targeted therapy.
 18. The method of claim 16, whereinthe first combination of therapies includes at least one therapyselected from the group consisting of: adoptive T-cell therapy,immunotherapy, and targeted therapy.
 19. The method of claim 16, whereinthe first combination of therapies includes a targeted therapy selectedfrom the group consisting of an immune checkpoint therapy, ananti-cancer vaccine therapy, and a T cell therapy.
 20. The method ofclaim 16, further comprising administering the first combination oftherapies to the patient.